Liana vs. tree wood decomposition
Njoroge D.M, Dossa G.G.O, Zuo J.
(Created on 2025 February 5 and last updated on 周四 10月 30 2025 10:50:32)
1 Introduction
Wood decomposition is important to redistribute nutrient within
ecosystems. But most of our knowledge of this process is based on
empirical research on one plant growth form. In tropical and subtropical
forests, the contribution of lianas another important plant growth form
may also be important to forest carbon and nutrient dynamics. Lianas
usually have high stem turnover rates and may produce softer wood with a
distinct chemical profile that may decay more rapidly than trees.
Although, numerous studies have examined chemical profile of tree and
liana leaves, to date, however, the chemical profile and afterlife and
decomposition dynamics of lianas remained unknown. In this experiment,
we attempt to fill this gap by directly contrasting wood decomposition
of 12 liana and 12 tree species in a tropical forest in China. We
hypothesized that: 1) liana wood decomposes faster than tree wood; 2)
liana wood characteristics differ systematically from tree wood; and 3)
microbial decay is higher for liana wood due to its larger vessels and
richer nutrients content, and thus have a stronger effect on the wood
decay of lianas compared to trees. We used a common garden as depicted
in the following figure S2 
.
Figure S2
2 Initial logs trait data before field incubation
Here we load the initial traits (from the file called “Experiment2_data_sheet.csv”) measured for each woody debris (WD) of the 12 species of liana and 12 species of trees. This includes, diameter, bark thickness, initial mass of each WD, the length of each WD used and the unique tag given to each WD before field incubation and the type of litterbag mesh size in which each WD was placed in. Moreover, for each WD, one initial disk was collected, the volumes (dry and wet) of the disk mass of fresh and dry disk (completed at 105 degrees) were measured. We later used this to measure the initial water content and compute the dry mass of each WD prior to incubation.
2.1 Data exploration
Let’s convert some variables to numeric and some to factors.
#convert to numeric
in_dat$diameter_1.size_cm<- as.numeric(in_dat$diameter_1.size_cm)
in_dat$diameter_2.size_cm<- as.numeric(in_dat$diameter_2.size_cm)
in_dat$diameter_3.size_cm<- as.numeric(in_dat$diameter_3.size_cm)
in_dat$Bark.thickness_1._mm<- as.numeric(in_dat$Bark.thickness_1._mm)
in_dat$Bark.thickness_2._mm<- as.numeric(in_dat$Bark.thickness_2._mm)
in_dat$Bark.thickness_3._mm<- as.numeric(in_dat$Bark.thickness_3._mm)
in_dat$Bark.thickness_4_mm<- as.numeric(in_dat$Bark.thickness_4_mm)
in_dat$Wet_weight_of_wood_block_g <- as.numeric(in_dat$Wet_weight_of_wood_block_g)
in_dat$Wet_weight_of_small_pieces_of_wood_g<- as.numeric(in_dat$Wet_weight_of_small_pieces_of_wood_g)
in_dat$Dry_weight_of_small_wood_blocks_g <-as.numeric(in_dat$Dry_weight_of_small_wood_blocks_g)
#convert to factors
in_dat $species<-as.factor(in_dat$species)
in_dat $growth_form<-as.factor(in_dat$growth_form)
in_dat$Tag<-as.factor(in_dat$Tag)
in_dat$species_label<-as.factor(in_dat$species_label)
in_dat$mesh_size<-as.factor(in_dat$mesh_size)
in_dat$family<-as.factor(in_dat$family)
in_dat$diameter_class<- as.factor(in_dat$diameter_class)
summary(in_dat)
dim(in_dat)2.1.1 Calculate average bark thickness
At the set up, we measured bark thickness at both ends of each log (two readings per end). Here we are going to calculate the average of bark thickness per log.
2.1.2 Does the average WD density vary with the growth forms?
One argument that might support to why lianas are likely to decompose at a faster rate than trees is the difference in wood densities. From literature, we see that trees have denser woods while lianas woody debris (WD) have wider vessels hence lower density. Here, we test whether this assumption of trees having higher density holds for our wood samples by calculating their wood density and ask: does the average WD wood density vary with the growth forms? We calculate the WD wood density as (mass/volume) for the dry WD subsections.
in_dat$density<- in_dat$Dry_weight_of_small_wood_blocks_g/in_dat$Dry_volume_of_small_wood_blocks.g
summary(in_dat)
in_dat$density<-as.numeric(in_dat$density)
#plot(density~ growth_form, data=in_dat)## Analysis of Variance Table
##
## Response: density
## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 4.8452 4.8452 430.7779 < 2.2e-16 ***
## diameter_class 1 0.1799 0.1799 15.9938 6.66e-05 ***
## growth_form:diameter_class 1 0.0071 0.0071 0.6327 0.4265
## Residuals 1520 17.0961 0.0112
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 4.845 4.845 430.778 < 2e-16 ***
## diameter_class 1 0.180 0.180 15.994 6.66e-05 ***
## growth_form:diameter_class 1 0.007 0.007 0.633 0.426
## Residuals 1520 17.096 0.011
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 12 observations deleted due to missingnessHere we plot this plot using ggplot for better visualization
sdiam<- in_dat[in_dat$diameter_class %in% c("2.5 cm"), ]
anvsm<- aov(density~growth_form,data=sdiam)
anova(anvsm)## Analysis of Variance Table
##
## Response: density
## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 2.2858 2.28584 210.59 < 2.2e-16 ***
## Residuals 780 8.4666 0.01085
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 2.286 2.2858 210.6 <2e-16 ***
## Residuals 780 8.467 0.0109
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingnesslblsdiam <- expression(paste( F["(1, 780)"]," = 210.6"))
dens_plotsmall<- ggplot(sdiam, aes(x = growth_form, y = density)) +
geom_jitter(aes(color = growth_form,shape=growth_form), size = 3, alpha = 0.8, width = 0.1)+ scale_y_continuous(limits = c(0,1))+
scale_shape_manual(values=c(15,17))+
geom_boxplot(outlier.shape = NA, alpha = 0) +
labs(y = bquote(bold("Woody debris density " (g/cm^3))), x = "Growth form",colour= "Growth form",title = "2.5 cm diameter WD") +
theme_classic() +
annotate("text", x = 1.5, y = 1, size=3, label = as.character(lblsdiam),parse=TRUE) +
annotate("text", x = 1.5, y = 0.95, size=3, label = "P < 0.001") +
theme(legend.position = "none" )+
theme(plot.title=element_text(
size=12, color="black", vjust = 1, hjust = 0.5),
axis.text = element_text(size=11, color="black", face = "bold"),
axis.title = element_text(size=12, color = "black",face = "bold"))+
scale_color_brewer(palette = "Set1")+
theme(text = element_text(size = 11,face="bold"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5))+
theme(strip.text.x = element_text(size = 10.5))
dens_plotsmall
bdiam<- in_dat[in_dat$diameter_class %in% c("5.0 cm"), ]
anvb<- aov(density~growth_form,data=bdiam)
anova(anvb)## Analysis of Variance Table
##
## Response: density
## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 2.5310 2.53097 217.03 < 2.2e-16 ***
## Residuals 740 8.6296 0.01166
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 2.531 2.5310 217 <2e-16 ***
## Residuals 740 8.630 0.0117
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 8 observations deleted due to missingnesslblbdiam<-expression(paste( F["(1, 740)"]," = 209.9"))
dens_plotbig<- ggplot(bdiam, aes(x = growth_form, y = density)) +
geom_jitter(aes(color = growth_form,shape=growth_form), size = 3, alpha = 0.8, width = 0.1)+ scale_y_continuous(limits = c(0,1))+
scale_shape_manual(values=c(15,17))+
geom_boxplot(outlier.shape = NA, alpha = 0) +
labs(y = bquote(bold("Woody debris density " (g/cm^3))), x = "Growth form",colour= "Growth form",title = "4.0 cm diameter WD") +
theme_classic() +
annotate("text", x = 1.5, y = 1, size=3, hjust=0.5,label = as.character(lblbdiam),parse=TRUE)+
annotate("text", x = 1.5, y = 0.95, size=3, label = "P < 0.001") +
theme(legend.position = "none" )+
theme(plot.title=element_text(
size=12, color="black", vjust = 1, hjust = 0.5),
axis.text = element_text(size=11, color="black"),
axis.title = element_text(size=12, color = "black"))+
scale_color_brewer(palette = "Set1")+
theme(text = element_text(size = 10,face="bold"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5))+
theme(strip.text.x = element_text(size = 10.5))
dens_plotbig
dens_plot<- dens_plotsmall+dens_plotbig
path<- getwd()
#ggsave(filename="dens_plot.png", plot=dens_plot, device="png",
# path=path, height=4, width=8, units="in", dpi=500)density_mean<- in_dat%>% group_by( growth_form ,species)%>% summarise(mean_density= mean(density, na.rm=TRUE))
density_mean## # A tibble: 24 × 3
## # Groups: growth_form [2]
## growth_form species mean_density
## <fct> <fct> <dbl>
## 1 Lianas Ayenia grandifolia 0.434
## 2 Lianas Bridelia stipularis 0.481
## 3 Lianas Calamus henryanus 0.408
## 4 Lianas Cayratia trifolia 0.483
## 5 Lianas Celastrus sp1 0.448
## 6 Lianas Celastrus sp2 0.461
## 7 Lianas Cheniella touranensis 0.470
## 8 Lianas Iodes vitiginea 0.448
## 9 Lianas Piper flaviflorum 0.477
## 10 Lianas Senegalia pruinescens 0.519
## # ℹ 14 more rows# Function to classify density within each growth form to low, medium or high
classify_density_by_A <- function(df) {
low_threshold <- quantile(df$mean_density, 0.33,na.rm=TRUE)
high_threshold <- quantile(df$mean_density, 0.66,na.rm=TRUE)
df$density_class <- ifelse(df$mean_density <= low_threshold, paste(unique(df$growth_form), "low"),
ifelse(df$mean_density <= high_threshold, paste(unique(df$growth_form), "medium"),
paste(unique(df$growth_form), "high")))
return(df)
}
# Apply the function to each group of A
library(dplyr)
data_density <- density_mean %>%
group_by(growth_form) %>%
group_modify(~ classify_density_by_A(.x)) %>%
ungroup() #important to ungroup after group_modify
data_density ## # A tibble: 24 × 4
## growth_form species mean_density density_class
## <fct> <fct> <dbl> <chr>
## 1 Lianas Ayenia grandifolia 0.434 " low"
## 2 Lianas Bridelia stipularis 0.481 " medium"
## 3 Lianas Calamus henryanus 0.408 " low"
## 4 Lianas Cayratia trifolia 0.483 " high"
## 5 Lianas Celastrus sp1 0.448 " low"
## 6 Lianas Celastrus sp2 0.461 " medium"
## 7 Lianas Cheniella touranensis 0.470 " medium"
## 8 Lianas Iodes vitiginea 0.448 " low"
## 9 Lianas Piper flaviflorum 0.477 " medium"
## 10 Lianas Senegalia pruinescens 0.519 " high"
## # ℹ 14 more rows2.1.3 Does the bark thickness vary with growth forms across the different wood diameter?
The other argument we gave as to why Lianas are likely to decompose at a faster rate than trees is the differences in bark proportions, from literature, we see that trees WD bark proportion is lower while lianas WD have higher bark proportion here we test whether this assumption holds for our wood samples by calculating their density does the average WD bark proportion vary with the growth forms does the bark thickness vary with growth forms across the different wood diameter
#calculate average diameter
in_dat$av_wd_diameter<- (in_dat$diameter_2.size_cm+in_dat$diameter_2.size_cm+in_dat$diameter_3.size_cm)/3
in_dat$wd_under_bark<- in_dat$av_wd_diameter-(in_dat$av_bark_thickness/10)in_dat%>% group_by(diameter_class)%>% summarise(mean=mean(av_wd_diameter, na.rm = TRUE),
sd= sd(av_wd_diameter, na.rm=TRUE),
se=sd(av_wd_diameter,na.rm = TRUE)/sqrt(n())
)## # A tibble: 2 × 4
## diameter_class mean sd se
## <fct> <dbl> <dbl> <dbl>
## 1 2.5 cm 2.59 0.538 0.0192
## 2 5.0 cm 3.92 0.965 0.0352## # A tibble: 2 × 2
## diameter_class median
## <fct> <dbl>
## 1 2.5 cm 2.53
## 2 5.0 cm 3.87Measuring bark percentage (check the link below) also see Berendt et al 2021 European Journal of Wood and Wood Products https://fennerschool-associated.anu.edu.au/mensuration/BrackandWood1998/BARK.HTM#. The proportion of fresh bark volume (Vbark) was calculated as the difference between the disc’s fresh volume over bark (Vo.b.) and under bark (Vu.b.) divided by Vo.b.:
The bark to inner wood diameter ratio (bark:wood ratio) was calculated by taking 2x the average bark thickness divided by the diameter of the inner wood.
in_dat$bark_wd_ratio<- 2*in_dat$av_bark_thickness/in_dat$wd_under_bark
#plot(bark_wd_ratio~ growth_form, data=in_dat)
av1<- aov(bark_wd_ratio~growth_form*diameter_class,data=in_dat)
anova(av1)## Analysis of Variance Table
##
## Response: bark_wd_ratio
## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 99.14 99.143 302.2137 <2e-16 ***
## diameter_class 1 2.15 2.154 6.5649 0.0105 *
## growth_form:diameter_class 1 0.02 0.019 0.0588 0.8085
## Residuals 1467 481.26 0.328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 99.1 99.14 302.214 <2e-16 ***
## diameter_class 1 2.2 2.15 6.565 0.0105 *
## growth_form:diameter_class 1 0.0 0.02 0.059 0.8085
## Residuals 1467 481.3 0.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 65 observations deleted due to missingness## Analysis of Variance Table
##
## Response: bark_wd_ratio
## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 99.14 99.143 302.4076 < 2e-16 ***
## diameter_class 1 2.15 2.154 6.5691 0.01048 *
## Residuals 1468 481.28 0.328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 99.1 99.14 302.408 <2e-16 ***
## diameter_class 1 2.2 2.15 6.569 0.0105 *
## Residuals 1468 481.3 0.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 65 observations deleted due to missingness## Analysis of Variance Table
##
## Response: bark_wd_ratio
## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 99.14 99.143 301.27 < 2.2e-16 ***
## Residuals 1469 483.43 0.329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 99.1 99.14 301.3 <2e-16 ***
## Residuals 1469 483.4 0.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 65 observations deleted due to missingnessBark to inner wood ratio is higher for liana species than trees WD.
lblbark_wood_ratio<-expression(paste( F["(1, 1469)"]," = 301.27"))
bark_wd_ratio_plot<- ggplot(in_dat, aes(x = growth_form, y = bark_wd_ratio)) +
geom_jitter(aes(color = growth_form,shape=growth_form), size = 3, alpha = 0.8, width = 0.1)+
scale_shape_manual(values=c(15,17))+
geom_boxplot(outlier.shape = NA, alpha = 0) +
labs(y = "Bark to inner wood ratio", x = "Growth forms",colour= "Growth forms") +
annotate("text", x = 1.5, y = 4.2, size=3, hjust=0.5,label = as.character(lblbark_wood_ratio),parse=TRUE)+
annotate("text", x = 1.5, y = 4, size=3, label = "P < 0.001") +
theme(legend.position = "none" )+
theme(plot.title=element_text(
size=12, color="black", vjust = 1, hjust = 0.5),
axis.text = element_text(size=11, color="black"),
axis.title = element_text(size=12, color = "black"))+
scale_color_brewer(palette = "Set1")+
theme(text = element_text(size = 10,face="bold"),
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5))
#bark_wd_ratio_plot2.1.4 Calculate average density and bark wd ratio for each species
After calculating the density, we now left join the two columns to our dataset.
2.1.5 Initial dry mass, final dry mass and % mass loss calculations
To avoid altering or changing the wood structure and properties before field incubation, we did not oven dry the logs. Instead to get the approximate dry mass prior to incubation for each log, we got 5cm discs from the logs and weighed their fresh weight, then the discs were oven dried to a constant mass and this was used to estimate the dry mass of the entire log (initial dry mass). The initial dry mass of the logs before field incubation were calculated as in (Seibold et al., 2021) using the following equation: Dry mass= [fresh mass](20 cm log)/[fresh mass](5cm disc) x [dry mass]_(5cm disc)
Percentage mass loss was calculated using the following equation:
ML=(M_initial-M_final)/M_initial x 100 Where M_initial represents the initial dry mass of the WD and M_final represents the final dry for the WD at the retrieval time.
3 Import the chemistry data for the logs before the start of the experiment
3.1 Initial chemical traits data
Each chemical trait [carbon (C), nitrogen (N), phosphorus (P), magnesium (Mg), manganese (Mn), calcium (Ca), potassium (K), silicon (Si), acid-detergent fiber (ADF), acid-detergent lignin (ADL), neutral fiber detergent (NDF), total sugars, condensed tannins)] were measured for each species for the two diameters used in this experiment (~2.0 cm and ~ 4.0 cm diameter). Wood and bark samples were grounded separately to measure the chemical traits. For some species there was no bark that could be used to measure the initial traits and are therefore missing. The initial chemistry is recorded in the file named “Exp1_initial_chemistry.csv”. Latter on, we explain how ADL, NDF, and ADF were used to get lignin, cellulose, and hemicellulose.
in_trait<-read.csv("Exp2_initial_Chemistry.csv", header = TRUE, sep = ",", quote = "\"",
dec = ".", fill = TRUE, comment.char = "",fileEncoding = 'latin1')
summary(in_trait)## species growth_form diameter_class wood_bark
## Length:135 Length:135 Length:135 Length:135
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## label lab_unique new_label T_C
## Length:135 Length:135 Length:135 Min. :396.0
## Class :character Class :character Class :character 1st Qu.:455.0
## Mode :character Mode :character Mode :character Median :472.0
## Mean :465.2
## 3rd Qu.:478.0
## Max. :507.0
##
## T_N Tannins NDF_per ADF_per
## Min. : 2.260 Min. :0.0100 Min. :50.00 Min. :40.25
## 1st Qu.: 5.510 1st Qu.:0.1200 1st Qu.:80.44 1st Qu.:62.10
## Median : 7.070 Median :0.3400 Median :84.32 Median :68.15
## Mean : 7.985 Mean :0.9055 Mean :83.14 Mean :67.38
## 3rd Qu.: 9.510 3rd Qu.:1.1250 3rd Qu.:89.58 3rd Qu.:73.07
## Max. :23.180 Max. :6.0000 Max. :93.34 Max. :80.34
## NA's :4
## ADL_per Ca_g_kg K_g_kg Mg_g_kg
## Min. : 7.21 Min. : 1.400 Min. : 1.470 Min. :0.290
## 1st Qu.:16.25 1st Qu.: 6.745 1st Qu.: 3.850 1st Qu.:0.695
## Median :18.97 Median :15.700 Median : 5.730 Median :1.220
## Mean :20.43 Mean :20.793 Mean : 7.325 Mean :1.377
## 3rd Qu.:22.96 3rd Qu.:28.560 3rd Qu.: 9.690 3rd Qu.:1.945
## Max. :39.82 Max. :76.660 Max. :25.550 Max. :3.600
##
## Mn_mg_kg P_g_kg T_sugar_per T_Si_g_kg
## Min. : 5.10 Min. :0.180 Min. :0.110 Min. : 0.07
## 1st Qu.: 12.65 1st Qu.:0.620 1st Qu.:0.370 1st Qu.: 0.46
## Median : 37.20 Median :0.830 Median :0.870 Median : 0.80
## Mean : 121.90 Mean :1.245 Mean :1.191 Mean : 2.36
## 3rd Qu.: 104.65 3rd Qu.:1.525 3rd Qu.:1.565 3rd Qu.: 1.86
## Max. :1283.80 Max. :4.300 Max. :7.220 Max. :31.58
## in_trait$species<- as.factor(in_trait$species)
#in_trait$ species_name<- as.factor((in_trait$ species_name))
in_trait$diameter_class<- as.factor(in_trait$diameter_class)
in_trait$wood_bark<-as.factor(in_trait$wood_bark)
in_trait$growth_form<-as.factor(in_trait$growth_form)Here we calculate cellulose and hemicellulose content.To do so, here we use the equations provided by Chen et al., 2012 plos one. Hemicellulose = Neutral-detergent fiber–Acid-detergent fiber; Cellulose = Acid-detergent fiber -Lignin; Total nonstructural carbohydrate = 1002Neutral-detergent fiber-Crude protein-Lipid-Ash.
4 Wood traits
We measured wood traits across woody and growth forms.
4.1 Carbon
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 240.365 240.365 1 69.093 4.0674 0.04761 *
## growth_form 79.244 79.244 1 22.045 1.3409 0.25926
## diameter_class:growth_form 187.347 187.347 1 69.093 3.1702 0.07939 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.06160753 0.7583601## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T_C ~ diameter_class + growth_form + (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: 704.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.1922 -0.2275 0.0348 0.2926 3.8509
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 170.07 13.041
## Residual 60.91 7.805
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 466.079 4.019 24.052 115.961 <2e-16 ***
## diameter_class5.0 cm 3.225 1.604 70.093 2.011 0.0482 *
## growth_formTree 6.475 5.560 22.045 1.165 0.2567
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.205
## grwth_frmTr -0.693 0.004## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 246.251 246.251 1 70.093 4.0425 0.04822 *
## growth_form 82.605 82.605 1 22.045 1.3561 0.25667
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 966 966.5 4.325 0.0403 *
## Residuals 93 20783 223.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblwC<-expression(paste( F["(1,93)"]," = 4.3"))
w_carb<- ggplot(data = in_traitw, aes(x =growth_form, y =T_C/10)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Total carbon (%)", fill= "Growth form", title = "a")+
annotate("text", x = 1.3, y = 50.5, size=5.5, hjust=0.5,label = as.character(lblwC), parse=TRUE) +
annotate("text", x = 1.3, y = 49.5, size=5.5, label = "p = 0.04") +
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_carb
This shows higher total carbon in tree wood compared to liana wood.
4.2 Nitrogen
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.015383 0.015383 1 69.026 1.1324 0.29097
## growth_form 0.081201 0.081201 1 22.011 5.9778 0.02295 *
## diameter_class:growth_form 0.004991 0.004991 1 69.026 0.3674 0.54639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.1902454 0.923689## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(T_N) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: -43.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8212 -0.3051 0.0162 0.3125 2.2337
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.13054 0.3613
## Residual 0.01346 0.1160
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.06232 0.10638 22.62003 19.386 1.38e-15 ***
## diameter_class5.0 cm -0.02529 0.02385 70.02564 -1.061 0.293
## growth_formTree -0.36518 0.14942 22.01061 -2.444 0.023 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.115
## grwth_frmTr -0.703 0.002## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.015143 0.015143 1 70.026 1.1248 0.2925
## growth_form 0.080414 0.080414 1 22.011 5.9732 0.0230 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 148.2 148.25 18.68 3.88e-05 ***
## Residuals 93 738.3 7.94
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblwN<-expression(paste( F["(1,93)"]," = 18.68"))
w_nit<- ggplot(
data = in_traitw, aes(x = growth_form, y =T_N/10)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Total nitrogen (%)", fill= "Growth form", title = "b")+
annotate("text", x = 1.6, y = 2, size=5.5, hjust=0.5,label = as.character(lblwN), parse=TRUE) +
annotate("text", x = 1.6, y = 1.85, size=5.5, label = "p <0.001") +
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_nit
Trees wood have less total nitrogen than liana wood. N does not change with diameter class.
4.3 Condensed tannins
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 1.34121 1.34121 1 66.949 5.0424 0.02804 *
## growth_form 0.01039 0.01039 1 21.925 0.0391 0.84514
## diameter_class:growth_form 0.02315 0.02315 1 66.949 0.0870 0.76888
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Tannins) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: 218.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2117 -0.3287 -0.0063 0.3416 3.6958
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 1.8233 1.3503
## Residual 0.2624 0.5122
## Number of obs: 93, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.2946 0.4012 22.8441 -3.226 0.00376 **
## diameter_class5.0 cm -0.2396 0.1064 67.9484 -2.252 0.02759 *
## growth_formTree 0.1115 0.5616 21.9261 0.199 0.84442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.136
## grwth_frmTr -0.702 0.003## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 1.33009 1.33009 1 67.948 5.0693 0.02759 *
## growth_form 0.01035 0.01035 1 21.926 0.0394 0.84442
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 1.79 1.785 1.659 0.201
## Residuals 91 97.93 1.076
## 2 observations deleted due to missingnessw_cd<- ggplot(data = in_traitw, aes(x = growth_form, y =Tannins)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Condensed tannins (%)", fill= "Growth form",title = "c")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_cd
This shows that together is significant difference of condensed tannin between diameters classes. Liana wood showed no difference in condensed tannin concentration compared to tree wood
4.4 Cellulose
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.403 0.403 1 69.058 0.1067 0.744952
## growth_form 56.886 56.886 1 22.029 15.0753 0.000801 ***
## diameter_class:growth_form 6.046 6.046 1 69.058 1.6021 0.209855
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.3583204 0.8909903## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cellulose ~ diameter_class + growth_form + (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: 461
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.65277 -0.48545 0.06363 0.45109 1.88003
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 18.428 4.293
## Residual 3.806 1.951
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 46.1063 1.2882 23.2235 35.790 < 2e-16 ***
## diameter_class5.0 cm -0.1234 0.4010 70.0582 -0.308 0.759195
## growth_formTree 6.9879 1.7978 22.0291 3.887 0.000793 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.160
## grwth_frmTr -0.699 0.003## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.360 0.360 1 70.058 0.0947 0.7591946
## growth_form 57.503 57.503 1 22.029 15.1080 0.0007928 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 1162 1162.3 54.85 5.79e-11 ***
## Residuals 93 1971 21.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblwCel<-expression(paste( F["(1,93)"]," = 13.34"))
w_cel<- ggplot(data = in_traitw, aes(x = growth_form, y =cellulose)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Cellulose (%)", fill= "Growth form", title = "d")+
annotate("text", x = 1.3, y = 60, size=5.5, hjust=0.5,label = as.character(lblwCel), parse=TRUE) +
annotate("text", x = 1.3, y = 58, size=5.5, label = "p < 0.001")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_cel
Tree wood has higher cellulose content than liana wood.
4.5 Lignin (ADL)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0000559 0.0000559 1 68.977 0.0094 0.9232
## growth_form 0.0001082 0.0001082 1 21.959 0.0181 0.8942
## diameter_class:growth_form 0.0148391 0.0148391 1 68.977 2.4847 0.1195## R2m R2c
## [1,] 0.0036666 0.8895338## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(ADL_per) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: -120.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.8683 -0.5969 -0.0503 0.3161 3.2846
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.048006 0.21910
## Residual 0.006093 0.07806
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.989376 0.064799 22.702645 46.133 <2e-16 ***
## diameter_class5.0 cm 0.001193 0.016045 69.975967 0.074 0.941
## growth_formTree -0.012557 0.090876 21.957619 -0.138 0.891
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.127
## grwth_frmTr -0.702 0.002## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 3.3702e-05 3.3702e-05 1 69.976 0.0055 0.9409
## growth_form 1.1635e-04 1.1635e-04 1 21.958 0.0191 0.8914## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 0.2 0.223 0.009 0.926
## Residuals 93 2391.1 25.711w_adl<- ggplot(data = in_traitw, aes(x = growth_form, y =ADL_per)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Lignin (%)", fill= "Growth form", title = "e")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_adl
Lignin does not vary with growth forms and neither with diameter.
4.6 Hemicellulose
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 3.0410 3.0410 1 69.017 2.4910 0.119073
## growth_form 11.8132 11.8132 1 21.992 9.6765 0.005097 **
## diameter_class:growth_form 1.0616 1.0616 1 69.017 0.8696 0.354318
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.2693525 0.8910145## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hemicellulose ~ diameter_class + growth_form + (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: 359.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4575 -0.4088 0.0447 0.3221 3.7470
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 6.955 2.637
## Residual 1.219 1.104
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 18.3496 0.7869 23.0121 23.318 < 2e-16 ***
## diameter_class5.0 cm 0.3555 0.2269 70.0178 1.567 0.12171
## growth_formTree -3.4278 1.1003 21.9928 -3.115 0.00504 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.148
## grwth_frmTr -0.700 0.003## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 2.9918 2.9918 1 70.018 2.4543 0.121714
## growth_form 11.8317 11.8317 1 21.993 9.7058 0.005041 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 289.3 289.35 37.48 2.18e-08 ***
## Residuals 93 717.9 7.72
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblwHemi<-expression(paste( F["(1,93)"]," = 37.48"))
w_hemc_plot<- ggplot(data = in_traitw, aes(x = growth_form, y =hemicellulose)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Hemicellulose (%)", fill= "Growth form", title = "f")+
annotate("text", x = 1.6, y = 24, size=5.5, hjust=0.5,label = as.character(lblwHemi), parse=TRUE) +
annotate("text", x = 1.6, y = 22.5, size=5.5, label = "p < 0.001")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_hemc_plot
Liana wood has higher hemicellulose level than tree wood
4.7 Calcium
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.27721 0.27721 1 69.03 4.0085 0.049202 *
## growth_form 0.88762 0.88762 1 22.01 12.8350 0.001659 **
## diameter_class:growth_form 0.02956 0.02956 1 69.03 0.4275 0.515401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.3315704 0.9179877## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Ca_g_kg) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: 99.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1433 -0.3880 -0.0283 0.3786 3.1893
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.49490 0.7035
## Residual 0.06858 0.2619
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.89444 0.20851 22.82173 13.882 1.3e-12 ***
## diameter_class5.0 cm -0.10873 0.05383 70.02913 -2.020 0.04722 *
## growth_formTree -1.04707 0.29220 22.00913 -3.583 0.00166 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.133
## grwth_frmTr -0.701 0.003## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.27982 0.27982 1 70.029 4.0802 0.047216 *
## growth_form 0.88061 0.88061 1 22.009 12.8406 0.001656 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 2246 2245.6 17.19 7.46e-05 ***
## Residuals 93 12149 130.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblwCa<-expression(paste( F["(1,93)"]," = 17.19"))
w_cal<- ggplot(data = in_traitw, aes(x = growth_form, y =Ca_g_kg)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Calcium g/kg", fill= "Growth form", title = "g")+
annotate("text", x = 1.3, y = 70, size=5.5, hjust=0.5,label = as.character(lblwCa), parse=TRUE) +
annotate("text", x = 1.3, y = 64, size=5.5, label = "p <0.001")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_cal
Liana wood has higher calcium content than tree wood
4.8 Potassium
av2<- lmer(log(in_traitw$K_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.065870 0.065870 1 69.004 4.6475 0.03459 *
## growth_form 0.062609 0.062609 1 21.999 4.4174 0.04725 *
## diameter_class:growth_form 0.056622 0.056622 1 69.004 3.9950 0.04958 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.1552977 0.9740135## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$K_g_kg) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: -10.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.85357 -0.49445 -0.02163 0.48426 2.19844
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.44691 0.6685
## Residual 0.01477 0.1216
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.04721 0.19423 22.19729 10.540 4.16e-10 ***
## diameter_class5.0 cm -0.05208 0.02499 70.00392 -2.084 0.0408 *
## growth_formTree -0.57499 0.27406 21.99899 -2.098 0.0476 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.066
## grwth_frmTr -0.706 0.001## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.064193 0.064193 1 70.004 4.3449 0.04077 *
## growth_form 0.065035 0.065035 1 21.999 4.4018 0.04761 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 329.3 329.3 13.02 0.000499 ***
## Residuals 93 2352.7 25.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblwK<-expression(paste( F["(1,93)"]," = 13.02"))
w_pot<- ggplot(data = in_traitw, aes(x = growth_form, y =K_g_kg)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Potassium g/kg", fill= "Growth form", title = "h")+
annotate("text", x = 1.6, y = 24, size=5.5, hjust=0.5,label = as.character(lblwK), parse=TRUE) +
annotate("text", x = 1.6, y = 22, size=5.5, label = "p < 0.001")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_pot
Liana wood has higher potassium content than tree wood
4.9 Magnesium
av2<- lmer(log(in_traitw$Mg_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0074797 0.0074797 1 69.024 0.2489 0.6194
## growth_form 0.0138037 0.0138037 1 22.012 0.4594 0.5050
## diameter_class:growth_form 0.0218273 0.0218273 1 69.024 0.7264 0.3970## R2m R2c
## [1,] 0.01866573 0.9271422## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$Mg_g_kg) ~ diameter_class + growth_form + (1 |
## species)
## Data: in_traitw
##
## REML criterion at convergence: 35.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.09992 -0.42232 0.04957 0.39121 2.49347
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.37468 0.6121
## Residual 0.02993 0.1730
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.02045 0.17944 22.48609 0.114 0.910
## diameter_class5.0 cm 0.01820 0.03556 70.02329 0.512 0.610
## growth_formTree -0.17066 0.25241 22.01156 -0.676 0.506
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.102
## grwth_frmTr -0.704 0.002## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.007843 0.007843 1 70.023 0.2620 0.6104
## growth_form 0.013684 0.013684 1 22.012 0.4571 0.5060## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 0.77 0.7706 1.96 0.165
## Residuals 93 36.56 0.3931w_mag<- ggplot(data = in_traitw, aes(x = growth_form, y =Mg_g_kg)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Magnesium g/kg", fill= "Growth form", title = "i")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_mag
There is no significance difference in liana and tree wood magnesium content
4.10 Managanese
av2<- lmer(log(log(in_traitw$Mn_mg_kg))~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0003616 0.0003616 1 69.024 0.0246 0.8759
## growth_form 0.0055796 0.0055796 1 22.006 0.3791 0.5444
## diameter_class:growth_form 0.0024372 0.0024372 1 69.024 0.1656 0.6853## R2m R2c
## [1,] 0.01475177 0.8967064## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(log(in_traitw$Mn_mg_kg)) ~ diameter_class + growth_form +
## (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: -38.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.35441 -0.48013 0.04549 0.53244 2.26634
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.12567 0.3545
## Residual 0.01454 0.1206
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.13355 0.10462 22.68835 10.834 1.93e-10 ***
## diameter_class5.0 cm -0.00377 0.02479 70.02319 -0.152 0.880
## growth_formTree 0.09056 0.14683 22.00638 0.617 0.544
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.122
## grwth_frmTr -0.702 0.002## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0003364 0.0003364 1 70.023 0.0231 0.8796
## growth_form 0.0055326 0.0055326 1 22.006 0.3804 0.5437## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 10133 10133 0.479 0.491
## Residuals 93 1967098 21152w_mang<- ggplot(data = in_traitw, aes(x = growth_form, y =Mn_mg_kg/1000)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Manganese g/kg", fill= "Growth form", title = "j")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_mang
Manganese content does not differ between liana and tree wood.
4.11 Phosphorous
av2<- lmer(log(in_traitw$P_g_kg+2)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.00084713 0.00084713 1 69.010 0.3530 0.5544
## growth_form 0.00000178 0.00000178 1 22.005 0.0007 0.9785
## diameter_class:growth_form 0.00081716 0.00081716 1 69.010 0.3405 0.5614## R2m R2c
## [1,] 0.0002740362 0.9674408## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$P_g_kg + 2) ~ diameter_class + growth_form + (1 |
## species)
## Data: in_traitw
##
## REML criterion at convergence: -178.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.60584 -0.36674 -0.02784 0.32741 3.14176
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.071289 0.26700
## Residual 0.002377 0.04876
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.130720 0.077577 22.204738 14.575 7.5e-13 ***
## diameter_class5.0 cm 0.006066 0.010022 70.009647 0.605 0.547
## growth_formTree -0.002895 0.109462 22.004678 -0.026 0.979
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.066
## grwth_frmTr -0.706 0.001## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 8.708e-04 8.708e-04 1 70.010 0.3663 0.5470
## growth_form 1.660e-06 1.660e-06 1 22.005 0.0007 0.9791## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 0.15 0.1505 0.156 0.694
## Residuals 93 89.78 0.9654w_ph<- ggplot(data = in_traitw, aes(x = growth_form, y =P_g_kg)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Phosphorous g/kg", fill= "Growth form", title = "k")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_ph
Phosphorous content does not significantly differ between liana and tree wood.
4.12 Total sugars
av2<- lmer(log(in_traitw$T_sugar_per+2)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.000566 0.000566 1 69.017 0.1310 0.718513
## growth_form 0.035532 0.035532 1 22.005 8.2278 0.008928 **
## diameter_class:growth_form 0.000009 0.000009 1 69.017 0.0021 0.963728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.2463279 0.9422445## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$T_sugar_per + 2) ~ diameter_class + growth_form +
## (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: -144.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8121 -0.2318 0.0231 0.3646 4.5056
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.052049 0.22814
## Residual 0.004257 0.06525
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.948391 0.066903 22.490040 14.176 1.07e-12 ***
## diameter_class5.0 cm -0.004896 0.013411 70.016567 -0.365 0.71613
## growth_formTree 0.269915 0.094099 22.004557 2.868 0.00893 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.103
## grwth_frmTr -0.704 0.002## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.000567 0.000567 1 70.017 0.1333 0.716133
## growth_form 0.035026 0.035026 1 22.005 8.2277 0.008928 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 19.85 19.85 27.19 1.11e-06 ***
## Residuals 93 67.90 0.73
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblwTsug<-expression(paste( F["(1,93)"]," = 27.19"))
w_sugar<- ggplot(data = in_traitw, aes(x = growth_form, y =T_sugar_per)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Total sugars (%)", fill= "Growth form", title = "l")+
annotate("text", x = 1.3, y = 4.2, size=5.5, hjust=0.5,label = as.character(lblwTsug), parse=TRUE) +
annotate("text", x = 1.3, y = 3.8, size=5.5, label = "p < 0.001")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_sugar
Tree wood has more total sugars than liana wood.
4.13 Silicon
av2<- lmer(log(in_traitw$T_Si_g_kg+2)~ diameter_class*growth_form +(1|species),data = in_traitw)
anova(av2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0149540 0.0149540 1 69.019 1.0252 0.3148
## growth_form 0.0002134 0.0002134 1 22.010 0.0146 0.9048
## diameter_class:growth_form 0.0006448 0.0006448 1 69.019 0.0442 0.8341## R2m R2c
## [1,] 0.001258852 0.9395454## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitw$T_Si_g_kg + 2) ~ diameter_class + growth_form +
## (1 | species)
## Data: in_traitw
##
## REML criterion at convergence: -27
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2038 -0.5694 -0.0084 0.5695 2.2151
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.22643 0.4758
## Residual 0.01439 0.1199
## Number of obs: 95, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.12284 0.13906 22.38821 8.075 4.45e-08 ***
## diameter_class5.0 cm 0.02507 0.02465 70.01886 1.017 0.313
## growth_formTree 0.02362 0.19582 22.00949 0.121 0.905
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.091
## grwth_frmTr -0.704 0.002## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0148703 0.0148703 1 70.019 1.0336 0.3128
## growth_form 0.0002092 0.0002092 1 22.009 0.0145 0.9051## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 5.7 5.737 0.694 0.407
## Residuals 93 768.4 8.263w_silicon<- ggplot(data = in_traitw, aes(x = growth_form, y =T_sugar_per)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Silicon (g/kg)", fill= "Growth form", title = "m")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
w_silicon
No difference across diameter nor across growth forms.
library(patchwork)
w_intrait_plot<- w_carb+w_nit+w_cd+w_cel+w_adl+w_hemc_plot+w_cal+w_pot+w_mag+w_mang+w_ph+w_sugar+w_silicon
## Let plot initial wood traits for lianas and trees. (a) Carbon, (b) Nitrogen, (c) condensed tannins, (d) cellulose, (e) lignin, (f) Hemicellulose, (g) Calcium, (h) Potassium (i) Magnesium, (j)manganese (k) Phosphorus, (l) Total sugars, (m) Silicon.
ggsave(filename="Figure S3.png", plot=w_intrait_plot, device="png",
path=path, height=12, width=15, units="in", dpi=500)5 Bark trait
We measured bark traits across woody and growth forms for species that have bark.
5.1 Carbon
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 62.376 62.376 1 17.107 1.8944 0.1865
## growth_form 25.797 25.797 1 20.092 0.7835 0.3866
## diameter_class:growth_form 1.164 1.164 1 17.107 0.0353 0.8531## R2m R2c
## [1,] 0.03521574 0.9646415## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T_C ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 316.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.30733 -0.17891 0.00287 0.27712 1.45225
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 863.4 29.384
## Residual 31.2 5.586
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 444.628 9.168 21.737 48.500 <2e-16 ***
## diameter_class5.0 cm 4.088 2.810 18.053 1.455 0.163
## growth_formTree 11.396 12.681 19.992 0.899 0.380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.214
## grwth_frmTr -0.698 0.037## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 66.030 66.030 1 18.053 2.1163 0.1629
## growth_form 25.199 25.199 1 19.992 0.8076 0.3795## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 2279 2279.0 2.858 0.0991 .
## Residuals 38 30301 797.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1b_carb<- ggplot(data = in_traitb, aes(x =growth_form, y =T_C/10)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Total carbon (%)", fill= "Growth form", title = "a")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_carb
There is no significant differences of total carbon between liana and tree bark.
5.2 Nitrogen
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0078425 0.0078425 1 17.839 0.5627 0.4630
## growth_form 0.0174376 0.0174376 1 19.804 1.2511 0.2767
## diameter_class:growth_form 0.0000501 0.0000501 1 17.839 0.0036 0.9528## R2m R2c
## [1,] 0.05257589 0.9315963## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(T_N) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 15.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.55550 -0.26883 -0.02211 0.23161 2.77756
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.17852 0.4225
## Residual 0.01321 0.1150
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.38741 0.13613 23.21718 17.538 6.85e-15 ***
## diameter_class5.0 cm -0.04516 0.05698 19.08750 -0.793 0.438
## growth_formTree -0.20852 0.18457 19.96852 -1.130 0.272
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.292
## grwth_frmTr -0.690 0.052## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0083006 0.0083006 1 19.087 0.6282 0.4378
## growth_form 0.0168656 0.0168656 1 19.968 1.2763 0.2720## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 54.0 54.03 2.434 0.127
## Residuals 38 843.7 22.20b_nit<- ggplot(
data = in_traitb, aes(x = growth_form, y =T_N/10)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Total nitrogen (%)", fill= "Growth form", title = "b")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_nit
Bark N does not change with diameter classes. Tree bark N not different from liana bark N.
5.3 Condensed tannins
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.11843 0.11843 1 18.814 0.6376 0.43456
## growth_form 0.64084 0.64084 1 19.665 3.4500 0.07829 .
## diameter_class:growth_form 0.00012 0.00012 1 18.814 0.0006 0.98032
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Tannins) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 99.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.84535 -0.33664 0.01907 0.23322 1.62178
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 1.6416 1.2813
## Residual 0.1754 0.4189
## Number of obs: 38, groups: species, 21
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -0.9480 0.4267 24.0028 -2.221 0.0360 *
## diameter_class5.0 cm 0.1808 0.2187 19.7520 0.827 0.4184
## growth_formTree 1.0830 0.5798 19.5087 1.868 0.0769 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.358
## grwth_frmTr -0.664 0.062## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.11986 0.11986 1 19.752 0.6832 0.41837
## growth_form 0.61223 0.61223 1 19.509 3.4895 0.07685 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 21.39 21.386 10.72 0.00234 **
## Residuals 36 71.81 1.995
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 2 observations deleted due to missingness## Analysis of Variance Table
##
## Response: Tannins
## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 21.386 21.3860 10.721 0.002345 **
## Residuals 36 71.810 1.9947
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblbTan<-expression(paste( F["(1,36)"]," = 10.72"))
b_cd<- ggplot(data = in_traitb, aes(x = growth_form, y =Tannins)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Condensed tannins (%)", fill= "Growth form",title = "c")+
annotate("text", x = 1.3, y = 5.5, size=5.5, hjust=0.5,label = as.character(lblbTan), parse=TRUE) +
annotate("text", x = 1.3, y = 4.9, size=5.5, label = "p = 0.002")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_cd
This shows that there is no significant differences of condensed tannin between diameters classes. Liana bark have lesser condensed tannin concentration than tree bark.
5.4 Cellulose
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.708 0.708 1 17.760 0.1091 0.74499
## growth_form 49.042 49.042 1 18.724 7.5657 0.01283 *
## diameter_class:growth_form 0.619 0.619 1 17.760 0.0955 0.76089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.2564428 0.9211743## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: cellulose ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 235.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.46359 -0.48893 0.07288 0.42758 1.32005
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 56.022 7.485
## Residual 6.004 2.450
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 35.9307 2.4742 23.6568 14.522 2.8e-13 ***
## diameter_class5.0 cm 0.5095 1.2002 19.2621 0.424 0.676
## growth_formTree 9.1567 3.3036 19.2630 2.772 0.012 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.338
## grwth_frmTr -0.684 0.061## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 1.082 1.082 1 19.262 0.1802 0.67591
## growth_form 46.126 46.126 1 19.263 7.6823 0.01205 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 676.7 676.7 13.34 0.000781 ***
## Residuals 38 1927.7 50.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblbCel<-expression(paste( F["(1,38)"]," = 13.34"))
b_cel<- ggplot(data = in_traitb, aes(x = growth_form, y =cellulose)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Cellulose (%)", fill= "Growth form", title = "d")+
annotate("text", x = 1.3, y = 55, size=5.5, hjust=0.5,label = as.character(lblbCel), parse=TRUE) +
annotate("text", x = 1.3, y = 51, size=5.5, label = "p < 0.001")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_cel
Liana bark has lesser cellulose than tree bark.
5.5 Lignin (ADL)
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.030638 0.030638 1 23.681 1.0385 0.318468
## growth_form 0.293775 0.293775 1 21.093 9.9578 0.004752 **
## diameter_class:growth_form 0.070988 0.070988 1 23.681 2.4062 0.134115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.3346431 0.8521086## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(ADL_per) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 22.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.63713 -0.25683 0.01545 0.32323 1.79775
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.10742 0.3277
## Residual 0.03056 0.1748
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.25574 0.12098 28.17289 26.912 < 2e-16 ***
## diameter_class5.0 cm -0.09629 0.08126 24.46949 -1.185 0.24745
## growth_formTree -0.50630 0.15222 20.51733 -3.326 0.00328 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.470
## grwth_frmTr -0.662 0.090## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.04290 0.04290 1 24.470 1.4039 0.247447
## growth_form 0.33807 0.33807 1 20.517 11.0630 0.003283 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 1016 1016.3 17.7 0.000152 ***
## Residuals 38 2182 57.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblbAdl<-expression(paste( F["(1,38)"]," = 17.7"))
b_adl<- ggplot(data = in_traitb, aes(x = growth_form, y =ADL_per)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Lignin (%)", fill= "Growth form", title = "e")+
annotate("text", x = 1.6, y = 38, size=5.5, hjust=0.5,label = as.character(lblbAdl), parse=TRUE) +
annotate("text", x = 1.6, y = 35, size=5.5, label = "p < 0.001")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_adl
Lignin (ADL) varies with growth forms and is higher in liana bark than trees.
5.6 Hemicellulose
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.01239 0.01239 1 17.380 0.0303 0.8638
## growth_form 0.42724 0.42724 1 20.066 1.0452 0.3188
## diameter_class:growth_form 0.66867 0.66867 1 17.380 1.6359 0.2177## R2m R2c
## [1,] 0.05771307 0.9555336## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hemicellulose ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 151
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.72789 -0.30004 0.00813 0.35328 1.68709
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 8.4700 2.9103
## Residual 0.4117 0.6416
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 13.709127 0.918006 22.265450 14.934 4.38e-13 ***
## diameter_class5.0 cm 0.003701 0.321164 18.407243 0.012 0.991
## growth_formTree -1.378562 1.261065 19.991834 -1.093 0.287
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.244
## grwth_frmTr -0.695 0.043## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.00005 0.00005 1 18.407 0.0001 0.9909
## growth_form 0.49199 0.49199 1 19.992 1.1950 0.2873## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 12.61 12.611 1.581 0.216
## Residuals 38 303.20 7.979b_hemc_plot<- ggplot(data = in_traitb, aes(x = growth_form, y =hemicellulose)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Hemicellulose (%)", fill= "Growth form", title = "f")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_hemc_plot
No differences across diameters nor across growth forms.
5.7 Calcium
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.007923 0.007923 1 18.115 0.3022 0.58923
## growth_form 0.100130 0.100130 1 20.066 3.8188 0.06476 .
## diameter_class:growth_form 0.010900 0.010900 1 18.115 0.4157 0.52716
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.1529171 0.9385233## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(Ca_g_kg) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 39.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.89996 -0.25797 -0.02341 0.21462 2.67072
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.33273 0.5768
## Residual 0.02556 0.1599
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.67943 0.18627 23.18238 19.753 5.29e-16 ***
## diameter_class5.0 cm -0.05086 0.07917 19.01995 -0.642 0.5283
## growth_formTree -0.50935 0.25220 19.82558 -2.020 0.0572 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.296
## grwth_frmTr -0.689 0.052## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.010549 0.010549 1 19.020 0.4127 0.52828
## growth_form 0.104259 0.104259 1 19.826 4.0787 0.05715 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 2093 2093.3 8.356 0.00632 **
## Residuals 38 9520 250.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblbCa<-expression(paste( F["(1,38)"]," = 8.3"))
b_cal<- ggplot(data = in_traitb, aes(x = growth_form, y =Ca_g_kg)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Calcium g/kg", fill= "Growth form", title = "g")+
annotate("text", x = 1.5, y = 75, size=5.5, hjust=0.5,label = as.character(lblbCa), parse=TRUE) +
annotate("text", x = 1.5, y = 68, size=5.5, label = "p = 0.006")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_cal
More calcium in liana bark than tree bark.
5.8 Potassium
av2<- lmer(log(in_traitb$K_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.001476 0.001476 1 19.992 0.0399 0.8438
## growth_form 0.091864 0.091864 1 20.433 2.4803 0.1306
## diameter_class:growth_form 0.042522 0.042522 1 19.992 1.1481 0.2967## R2m R2c
## [1,] 0.1174186 0.891453## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitb$K_g_kg) ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 42
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.39082 -0.38256 0.03438 0.32239 2.46075
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.25622 0.5062
## Residual 0.03841 0.1960
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.03337 0.17247 25.69391 11.790 7.27e-12 ***
## diameter_class5.0 cm -0.02905 0.09464 21.32414 -0.307 0.762
## growth_formTree -0.38754 0.22632 20.25780 -1.712 0.102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.383
## grwth_frmTr -0.677 0.070## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.003618 0.003618 1 21.324 0.0942 0.7619
## growth_form 0.112626 0.112626 1 20.258 2.9322 0.1021## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 128.3 128.3 5.834 0.0206 *
## Residuals 38 836.0 22.0
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblbK<-expression(paste( F["(1,38)"]," = 5.83"))
b_pot<- ggplot(data = in_traitb, aes(x = growth_form, y =K_g_kg)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Potassium g/kg", fill= "Growth form", title = "h")+
annotate("text", x = 1.6, y = 20, size=5.5, hjust=0.5,label = as.character(lblbK), parse=TRUE) +
annotate("text", x = 1.6, y = 18, size=5.5, label = "p = 0.02")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_pot
More potassium in liana bark than tree bark.
5.9 Magnesium
av2<- lmer(log(in_traitb$Mg_g_kg)~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0245715 0.0245715 1 17.506 1.5274 0.2328
## growth_form 0.0001577 0.0001577 1 19.909 0.0098 0.9221
## diameter_class:growth_form 0.0066943 0.0066943 1 17.506 0.4161 0.5272## R2m R2c
## [1,] 0.006964865 0.9433095## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(in_traitb$Mg_g_kg) ~ diameter_class + growth_form + (1 |
## species)
## Data: in_traitb
##
## REML criterion at convergence: 26.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7869 -0.3866 -0.0071 0.3462 1.9990
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.25958 0.5095
## Residual 0.01596 0.1263
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.58787 0.16248 22.72210 3.618 0.00147 **
## diameter_class5.0 cm -0.08388 0.06292 18.71061 -1.333 0.19847
## growth_formTree -0.03248 0.22168 19.93586 -0.147 0.88499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.270
## grwth_frmTr -0.692 0.047## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0283674 0.0283674 1 18.711 1.7774 0.1985
## growth_form 0.0003426 0.0003426 1 19.936 0.0215 0.8850## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 0.044 0.0438 0.058 0.81
## Residuals 38 28.554 0.7514b_mag<- ggplot(data = in_traitb, aes(x = growth_form, y =Mg_g_kg)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Magnesium g/kg", fill= "Growth form", title = "i")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_mag
No difference across diameters nor across growth forms.
5.10 Manganese
av2<- lmer(log(log(in_traitb$Mn_mg_kg))~ diameter_class*growth_form +(1|species),data = in_traitb)
anova(av2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.003802 0.003802 1 25.846 0.1323 0.7190
## growth_form 0.085361 0.085361 1 18.961 2.9710 0.1010
## diameter_class:growth_form 0.068354 0.068354 1 25.846 2.3791 0.1351## R2m R2c
## [1,] 0.163154 0.7069552## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(log(in_traitb$Mn_mg_kg)) ~ diameter_class + growth_form +
## (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 11.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1929 -0.2308 0.0632 0.2838 1.8516
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.06176 0.2485
## Residual 0.02748 0.1658
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.29650 0.09893 28.54959 13.105 1.33e-13 ***
## diameter_class5.0 cm 0.04209 0.07437 25.96757 0.566 0.5763
## growth_formTree 0.22967 0.12026 19.08876 1.910 0.0713 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.526
## grwth_frmTr -0.650 0.105## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.008801 0.008801 1 25.968 0.3203 0.5763
## growth_form 0.100233 0.100233 1 19.089 3.6474 0.0713 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 758842 758842 9.126 0.00449 **
## Residuals 38 3159750 83151
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblbMn<-expression(paste( F["(1,38)"]," = 9.12"))
b_mang<- ggplot(data = in_traitb, aes(x = growth_form, y =Mn_mg_kg/1000)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Manganese g/kg", fill= "Growth form", title = "j")+
annotate("text", x = 1.3, y = 1.2, size=5.5, hjust=0.5,label = as.character(lblbMn), parse=TRUE) +
annotate("text", x = 1.3, y = 1.1, size=5.5, label = "p = 0.04")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_mang
More manganese in tree bark than liana bark.
5.11 Phosphorous
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.0012395 0.0012395 1 17.215 0.0462 0.8324
## growth_form 0.0000001 0.0000001 1 20.290 0.0000 0.9989
## diameter_class:growth_form 0.0073247 0.0073247 1 17.215 0.2729 0.6080## R2m R2c
## [1,] 0.0006769956 0.9666669## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: P_g_kg ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 55.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.94394 -0.14782 -0.08468 0.14070 2.15817
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.77348 0.8795
## Residual 0.02589 0.1609
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.19423 0.27373 21.81149 4.363 0.000253 ***
## diameter_class5.0 cm -0.01273 0.08102 18.17461 -0.157 0.876879
## growth_formTree 0.01135 0.37921 20.18898 0.030 0.976409
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.206
## grwth_frmTr -0.698 0.036## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.00063909 0.00063909 1 18.175 0.0247 0.8769
## growth_form 0.00002320 0.00002320 1 20.189 0.0009 0.9764## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 0.24 0.2388 0.25 0.62
## Residuals 38 36.32 0.9558b_ph<- ggplot(data = in_traitb, aes(x = growth_form, y =P_g_kg)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Phosphorous g/kg", fill= "Growth form", title = "k")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_ph
No differences across diameters nor across growth forms.
5.12 Total sugars
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.1360 0.1360 1 26.458 0.2564 0.616788
## growth_form 5.1785 5.1785 1 21.458 9.7608 0.005038 **
## diameter_class:growth_form 0.0001 0.0001 1 26.458 0.0003 0.987312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## R2m R2c
## [1,] 0.2870332 0.7830753## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T_sugar_per ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 119.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.57045 -0.16438 -0.05406 0.18881 2.52091
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 1.2076 1.0989
## Residual 0.5076 0.7124
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.4692 0.4329 29.5879 1.084 0.28713
## diameter_class5.0 cm 0.1650 0.3212 27.0393 0.514 0.61161
## growth_formTree 1.6788 0.5286 20.8515 3.176 0.00458 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.519
## grwth_frmTr -0.651 0.103## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.1340 0.1340 1 27.039 0.2639 0.611609
## growth_form 5.1205 5.1205 1 20.852 10.0886 0.004575 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 30.97 30.970 18.36 0.00012 ***
## Residuals 38 64.11 1.687
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1lblbTsug<-expression(paste( F["(1,38)"]," = 18.36"))
b_sugars<- ggplot(data = in_traitb, aes(x = growth_form, y =T_sugar_per)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Total sugars (%)", fill= "Growth form", title = "l")+
annotate("text", x = 1.3, y = 6, size=5.5, hjust=0.5,label = as.character(lblbMn), parse=TRUE) +
annotate("text", x = 1.3, y = 5.8, size=5.5, label = "p = 0.0001")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_sugars
More sugars in tree bark than liana bark.
5.13 Silicon
## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 0.82917 0.82917 1 16.167 1.6328 0.2194
## growth_form 0.12951 0.12951 1 19.974 0.2550 0.6191
## diameter_class:growth_form 0.76069 0.76069 1 16.167 1.4980 0.2385## R2m R2c
## [1,] 0.01334417 0.9929902## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: T_Si_g_kg ~ diameter_class + growth_form + (1 | species)
## Data: in_traitb
##
## REML criterion at convergence: 197.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.79741 -0.31800 -0.02842 0.29826 1.89585
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 71.1129 8.4328
## Residual 0.5214 0.7221
## Number of obs: 40, groups: species, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.1539 2.5611 20.3284 1.231 0.232
## diameter_class5.0 cm 0.5269 0.3676 17.1663 1.433 0.170
## growth_formTree 1.9305 3.6047 19.9471 0.536 0.598
##
## Correlation of Fixed Effects:
## (Intr) d_5.0c
## dmtr_cl5.0c -0.100
## grwth_frmTr -0.705 0.017## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## diameter_class 1.07096 1.07096 1 17.166 2.0539 0.1698
## growth_form 0.14956 0.14956 1 19.947 0.2868 0.5982## Df Sum Sq Mean Sq F value Pr(>F)
## growth_form 1 4.3 4.32 0.075 0.785
## Residuals 38 2182.8 57.44b_silicon<- ggplot(data = in_traitb, aes(x = growth_form, y =T_sugar_per)) +
geom_boxplot(aes(fill = growth_form), color = "black")+ scale_fill_manual(values = c("lightcoral", "#00E5EE"))+
labs(x= "Growth form", y= "Silicon (g/kg)", fill= "Growth form", title = "m")+
theme_bw()+
theme(text = element_text(size = 14,face="bold"),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5),
axis.text = element_text(size=12, face= "bold"))+ theme(legend.position = "none")
b_silicon
No differences between diameters nor across growth forms.
library(patchwork)
b_intrait_plot<- b_carb+b_nit+b_cd+b_cel+b_adl+b_hemc_plot+b_cal+b_pot+b_mag+b_mang+b_ph+b_sugars+b_silicon
#b_intrait_plot
## Initial bark traits across different wood tissue for lianas and trees. (a) Carbon, (b) Nitrogen, (c) condensed tannins (d) cellulose, (e) lignin, (f) Hemicellulose, (g) Calcium, (h) Potassium, (i) Magnesium, (j) Manganese, (k) Phosphorus, (l) Total sugars, (m) Silicon.
ggsave(filename="Figure S4.png", plot=b_intrait_plot, device="png",
path=path, height=12, width=15, units="in", dpi=500)Initial traits do not vary between the diameter classes so we can instead use the overall average mean per species.
lev<- c("Calamus henryanus","Ayenia grandifolia","Celastrus sp1","Iodes vitiginea","Celastrus sp2","Cheniella touranensis","Piper flaviflorum",
"Bridelia stipularis","Cayratia trifolia","Ventilago leiocarpa","Senegalia pruinescens","Urceola rosea","Kleinhovia hospita","Macaranga denticulata","Thyrsostachys siamensis","Bauhinia purpurea",
"Toona ciliata","Piper umbellatum","Cunninghamia lanceolata","Tectona grandis","Ficus altissima","Eucalyptus citriodora","Shorea assamica","Mesua ferrea")
in_trait$species<- factor(in_trait$species,levels = lev)
levels(in_trait$species)## [1] "Calamus henryanus" "Ayenia grandifolia"
## [3] "Celastrus sp1" "Iodes vitiginea"
## [5] "Celastrus sp2" "Cheniella touranensis"
## [7] "Piper flaviflorum" "Bridelia stipularis"
## [9] "Cayratia trifolia" "Ventilago leiocarpa"
## [11] "Senegalia pruinescens" "Urceola rosea"
## [13] "Kleinhovia hospita" "Macaranga denticulata"
## [15] "Thyrsostachys siamensis" "Bauhinia purpurea"
## [17] "Toona ciliata" "Piper umbellatum"
## [19] "Cunninghamia lanceolata" "Tectona grandis"
## [21] "Ficus altissima" "Eucalyptus citriodora"
## [23] "Shorea assamica" "Mesua ferrea"df<-in_trait %>% arrange(factor(species, levels = lev))
mean_trait<- in_trait %>% group_by(species, wood_bark)%>%
summarise(Carbon= mean(T_C,na.rm = TRUE)/10, cellulose=mean(cellulose,na.rm=TRUE),
Nitrogen= mean(T_N/10,na.rm = TRUE)/10,
hemicellulose= mean(hemicellulose,na.rm=TRUE),
Tannins= mean(Tannins,na.rm = TRUE), ADL=mean(ADL_per,na.rm=TRUE),
Calcium=mean(Ca_g_kg, na.rm=TRUE),
Potassium= mean(K_g_kg, na.rm=TRUE),
magesium= mean(Mg_g_kg, na.rm=TRUE),
Manganese= mean(Mn_mg_kg, na.rm=TRUE),
Phosphorous= mean(P_g_kg, narm=TRUE),
sugars= mean(T_sugar_per, narm=TRUE),
Silicon= mean(T_Si_g_kg, na.rm= TRUE)
)
mean_trait$species<- as.factor(mean_trait$species)
mean_trait$species<- factor(mean_trait$species, levels = lev)
df <- mean_trait[order(levels(mean_trait$species)),]
levels(in_trait$species)## [1] "Calamus henryanus" "Ayenia grandifolia"
## [3] "Celastrus sp1" "Iodes vitiginea"
## [5] "Celastrus sp2" "Cheniella touranensis"
## [7] "Piper flaviflorum" "Bridelia stipularis"
## [9] "Cayratia trifolia" "Ventilago leiocarpa"
## [11] "Senegalia pruinescens" "Urceola rosea"
## [13] "Kleinhovia hospita" "Macaranga denticulata"
## [15] "Thyrsostachys siamensis" "Bauhinia purpurea"
## [17] "Toona ciliata" "Piper umbellatum"
## [19] "Cunninghamia lanceolata" "Tectona grandis"
## [21] "Ficus altissima" "Eucalyptus citriodora"
## [23] "Shorea assamica" "Mesua ferrea"# function to calculate mean plus/minus std error
meanse <- function(x, ...){
mean1 <- signif(round(mean(x, na.rm=T),2), 3) #calculate mean and round
se1 <- signif(round(sd(x, na.rm=T)/sqrt(sum(!is.na(x))), 2),2) # std error - round adding zeros
out <- paste(mean1, "$\\pm$", se1) # paste together mean plus/minus and standard error
if (str_detect(out,"NA")) {out="NA"} # if missing do not add plusminus
return(out)
}
in_trait$Nitrogen<- in_trait$T_N/10
in_trait$Carbon<- in_trait$T_C/10
# select columns
# then form grouping variables
# then calculate summary statistics using function
t1 <- in_trait %>% dplyr::select(c(species, growth_form, wood_bark,Carbon, Nitrogen, Tannins, ADL_per, cellulose, hemicellulose, Ca_g_kg, K_g_kg,Mg_g_kg, Mn_mg_kg,P_g_kg,T_sugar_per,T_Si_g_kg)) %>%
group_by(species, growth_form, wood_bark) %>%
summarise_all(.funs = meanse)
t1b<-t1 %>% arrange(factor(species, levels = lev))
library(readr) # reading in the data
library(kableExtra) # make HTML tables
library(tidyverse) # for stacking and selecting
library(dplyr)
library(tidyr)
traits_wide <- pivot_wider(mean_trait, names_from = c(wood_bark), values_from = c(Carbon:Silicon))
traits_wide## # A tibble: 24 × 27
## # Groups: species [24]
## species Carbon_wood Carbon_bark cellulose_wood cellulose_bark Nitrogen_wood
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Calamus … 48.0 NA 52.6 NA 0.0571
## 2 Ayenia g… 47.6 41.6 46.6 36.9 0.0678
## 3 Celastru… 45.7 45.4 39.4 27.9 0.0857
## 4 Iodes vi… 46.5 44.8 46.4 38.9 0.0939
## 5 Celastru… 46.0 44.4 44.3 19.8 0.063
## 6 Cheniell… 45.4 40.2 49.1 47.4 0.0762
## 7 Piper fl… 44.2 42.2 40.7 33.7 0.104
## 8 Bridelia… 47.6 46.2 50.4 46.7 0.0529
## 9 Cayratia… 46.7 49.6 45.3 38.4 0.107
## 10 Ventilag… 48.0 44.0 46.3 28.9 0.0677
## # ℹ 14 more rows
## # ℹ 21 more variables: Nitrogen_bark <dbl>, hemicellulose_wood <dbl>,
## # hemicellulose_bark <dbl>, Tannins_wood <dbl>, Tannins_bark <dbl>,
## # ADL_wood <dbl>, ADL_bark <dbl>, Calcium_wood <dbl>, Calcium_bark <dbl>,
## # Potassium_wood <dbl>, Potassium_bark <dbl>, magesium_wood <dbl>,
## # magesium_bark <dbl>, Manganese_wood <dbl>, Manganese_bark <dbl>,
## # Phosphorous_wood <dbl>, Phosphorous_bark <dbl>, sugars_wood <dbl>, …The initial traits are presented in the Table S1 as the means for each species. Since there was no significance difference between the traits across diameters, then the means for each trait is the mean of both diameters values.
6 Join intial traits to the datasheet file
in_dat2<- in_dat%>% left_join(traits_wide, by= c("species"))
in_datb<- in_dat2%>% left_join(data_density, by= c("species","growth_form"))
head(in_datb)## species_label species family Tag growth_form
## 1 F58 Cunninghamia lanceolata <NA> T0561 Trees
## 2 F58 Cunninghamia lanceolata <NA> T0573 Trees
## 3 F58 Cunninghamia lanceolata <NA> T0577 Trees
## 4 F58 Cunninghamia lanceolata <NA> T0565 Trees
## 5 F58 Cunninghamia lanceolata <NA> T0571 Trees
## 6 F58 Cunninghamia lanceolata <NA> T0567 Trees
## mesh_size diameter_class diameter_1.size_cm diameter_2.size_cm
## 1 Invertebrates access 5.0 cm 3.2 3.2
## 2 Invertebrates access 5.0 cm 3.3 3.3
## 3 Invertebrates access 5.0 cm 3.6 3.8
## 4 Invertebrates access 5.0 cm 3.6 3.7
## 5 Invertebrates access 5.0 cm 2.9 3.1
## 6 Invertebrates access 5.0 cm 2.7 2.9
## diameter_3.size_cm Bark.thickness_1._mm Bark.thickness_2._mm
## 1 3.1 0.97 0.48
## 2 3.5 1.09 1.24
## 3 3.7 2.64 1.71
## 4 3.8 1.80 1.86
## 5 3.0 1.31 6.04
## 6 3.1 1.38 1.14
## Bark.thickness_3._mm Bark.thickness_4_mm length_cm Wet_weight_of_wood_block_g
## 1 0.99 0.66 19.5 150.78
## 2 0.89 1.08 20.0 161.74
## 3 2.37 2.19 20.5 205.59
## 4 1.36 1.15 20.5 207.58
## 5 0.78 0.84 19.5 135.11
## 6 1.31 1.12 20.5 127.85
## Wet_weight_of_small_pieces_of_wood_g Volume_of_wet_small_pieces.g
## 1 14.18 15.84
## 2 17.40 20.15
## 3 22.80 25.52
## 4 21.16 23.90
## 5 13.36 15.82
## 6 16.38 19.38
## Dry_weight_of_small_wood_blocks_g Dry_volume_of_small_wood_blocks.g
## 1 8.14 13.79
## 2 10.09 17.52
## 3 12.06 21.43
## 4 12.04 20.90
## 5 8.49 14.21
## 6 9.31 16.83
## av_bark_thickness density av_wd_diameter wd_under_bark bark_wd_ratio
## 1 0.7750 0.5902828 3.166667 3.089167 0.5017534
## 2 1.0750 0.5759132 3.366667 3.259167 0.6596778
## 3 2.2275 0.5627625 3.766667 3.543917 1.2570837
## 4 1.5425 0.5760766 3.733333 3.579083 0.8619525
## 5 2.2425 0.5974666 3.066667 2.842417 1.5778827
## 6 1.2375 0.5531788 2.966667 2.842917 0.8705848
## initial_mass Carbon_wood Carbon_bark cellulose_wood cellulose_bark
## 1 86.55495 50.475 49.8 43.3975 41.68
## 2 93.79061 50.475 49.8 43.3975 41.68
## 3 108.74629 50.475 49.8 43.3975 41.68
## 4 118.11263 50.475 49.8 43.3975 41.68
## 5 85.85957 50.475 49.8 43.3975 41.68
## 6 72.66688 50.475 49.8 43.3975 41.68
## Nitrogen_wood Nitrogen_bark hemicellulose_wood hemicellulose_bark
## 1 0.02845 0.0643 11.6775 9.1
## 2 0.02845 0.0643 11.6775 9.1
## 3 0.02845 0.0643 11.6775 9.1
## 4 0.02845 0.0643 11.6775 9.1
## 5 0.02845 0.0643 11.6775 9.1
## 6 0.02845 0.0643 11.6775 9.1
## Tannins_wood Tannins_bark ADL_wood ADL_bark Calcium_wood Calcium_bark
## 1 0.4875 1.02 35.6825 30.51 5.1 12.84
## 2 0.4875 1.02 35.6825 30.51 5.1 12.84
## 3 0.4875 1.02 35.6825 30.51 5.1 12.84
## 4 0.4875 1.02 35.6825 30.51 5.1 12.84
## 5 0.4875 1.02 35.6825 30.51 5.1 12.84
## 6 0.4875 1.02 35.6825 30.51 5.1 12.84
## Potassium_wood Potassium_bark magesium_wood magesium_bark Manganese_wood
## 1 1.865 3.56 0.34 0.79 401.3
## 2 1.865 3.56 0.34 0.79 401.3
## 3 1.865 3.56 0.34 0.79 401.3
## 4 1.865 3.56 0.34 0.79 401.3
## 5 1.865 3.56 0.34 0.79 401.3
## 6 1.865 3.56 0.34 0.79 401.3
## Manganese_bark Phosphorous_wood Phosphorous_bark sugars_wood sugars_bark
## 1 681.3 0.29 0.66 0.4475 0.99
## 2 681.3 0.29 0.66 0.4475 0.99
## 3 681.3 0.29 0.66 0.4475 0.99
## 4 681.3 0.29 0.66 0.4475 0.99
## 5 681.3 0.29 0.66 0.4475 0.99
## 6 681.3 0.29 0.66 0.4475 0.99
## Silicon_wood Silicon_bark mean_density density_class
## 1 0.175 2.1 0.5611632 medium
## 2 0.175 2.1 0.5611632 medium
## 3 0.175 2.1 0.5611632 medium
## 4 0.175 2.1 0.5611632 medium
## 5 0.175 2.1 0.5611632 medium
## 6 0.175 2.1 0.5611632 medium7 Import harvest 1, 2, 3 and 4 (6, 12, 18 and 24 months) data
Here we import data of he remaining logs at retrieval times in the experiment (after 6, 12, 18,and 24 months). Please see below the explanation on the data in each column.
1- Tag – The unique log id number. Id number starting with L is liana log while T is tree log.
2- log_fresh_weight- The fresh mass of the entire wood piece (bark + xylem) at the end of decomposition period.
3- wood_weight- The fresh mass of only the xylem after the bark is removed.
4- Subsection- The number of the 2cm discs that were cut from the retrieved log. For each log we have two discs either 1 or 2.
5- disk_fresh_weight- The fresh mass of the 2 cm disc after decomposition.
6- disk_fresh_volume- The volume of the fresh 2 cm disc.
7- disk_dry_weight- This is the dry mass of the disc after oven drying.
8- disk_dry_volume- The volume of the oven dried 2 cm discs.
9- Bark_fresh_mass_at_harvest- The fresh mass of the bark at harvest (it’s the difference between log_fresh_weight and wood_weight)
10- Bark_fresh_volume_at_harvest- The fresh volume of the bark subsection
11- Dry_bark_mass_at_harvest- The dry mass of the bark subsection after oven drying.
12- Dry_bark_volume_at_harvest- The volume of he dry bark subsections
13- Harvest- The harvest plot number (1-4) from which the wood was taken from.
14- Time_months- The number of months between start of the experiment and the wood retrieval date (6, 12, 18 or 24 months)
15- incubation_time- same as Time_months column
16- block- The replicate plot (block 4) from which the log was incubated at.
For the last harvest (24 months) we separated the wood from the bark for all retrieved samples to get an approximate of the proportions of wood and bark remaining at the end of the experiment.
dat<-read.csv("Experiment2_harvest1-4.csv", header = TRUE, sep = ",", quote = "\"",
dec = ".", fill = TRUE, comment.char = "",fileEncoding = 'latin1')
summary(dat)## date Tag log_fresh_weight wood_weight
## Length:2844 Length:2844 Min. : 0.00 Min. : 0.000
## Class :character Class :character 1st Qu.: 24.43 1st Qu.: 0.000
## Mode :character Mode :character Median : 48.65 Median : 0.000
## Mean : 63.32 Mean : 9.602
## 3rd Qu.: 87.17 3rd Qu.: 0.000
## Max. :386.32 Max. :182.390
## NA's :5
## subsection disk_fresh_weight disk_fresh_volume disk_dry_weight
## Min. :1.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 2.303 1st Qu.: 4.97 1st Qu.: 1.370
## Median :1.000 Median : 4.635 Median : 9.08 Median : 2.875
## Mean :1.457 Mean : 6.095 Mean : 11.53 Mean : 3.763
## 3rd Qu.:2.000 3rd Qu.: 8.258 3rd Qu.: 15.36 3rd Qu.: 5.067
## Max. :2.000 Max. :55.430 Max. :474.00 Max. :35.260
## NA's :6 NA's :7 NA's :6
## disk_dry_volume Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
## Min. : 0.00 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 4.23 1st Qu.: 0.000 1st Qu.: 0.0000
## Median : 7.92 Median : 0.000 Median : 0.0000
## Mean : 11.29 Mean : 1.076 Mean : 0.7675
## 3rd Qu.: 13.54 3rd Qu.: 0.000 3rd Qu.: 0.0000
## Max. :3921.00 Max. :39.560 Max. :35.6400
## NA's :9 NA's :10 NA's :4
## Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest Harvest
## Min. : 0.0000 Min. : 0.0000 Min. :1.00
## 1st Qu.: 0.0000 1st Qu.: 0.0000 1st Qu.:1.00
## Median : 0.0000 Median : 0.0000 Median :2.00
## Mean : 0.5704 Mean : 0.5476 Mean :2.45
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 3rd Qu.:3.00
## Max. :16.7900 Max. :30.8600 Max. :4.00
## NA's :5 NA's :6 NA's :4
## Time_months incubation_time block
## Length:2844 Min. : 6.0 Min. :1.000
## Class :character 1st Qu.: 6.0 1st Qu.:1.000
## Mode :character Median :12.0 Median :2.000
## Mean :14.7 Mean :2.477
## 3rd Qu.:18.0 3rd Qu.:3.000
## Max. :24.0 Max. :4.000
## NA's :4 NA's :4After harvest, we had two discs for each of the wood. Here we combine them together and find the mean for the two discs and use them to calculate the wood and bark dry mass at harvest time.
dat$Tag<-as.factor(dat$Tag)
dat$block<-as.factor(dat$block)
dat$Harvest<-as.factor(dat$Harvest)
dat$Time_months<-as.factor(dat$Time_months)
dat$incubation_time<-as.factor(dat$incubation_time)
dat$disk_fresh_weight<-as.numeric(dat$disk_fresh_weight)
summary(dat)## date Tag log_fresh_weight wood_weight
## Length:2844 L0817 : 4 Min. : 0.00 Min. : 0.000
## Class :character T0358 : 4 1st Qu.: 24.43 1st Qu.: 0.000
## Mode :character L1253 : 3 Median : 48.65 Median : 0.000
## L0102 : 2 Mean : 63.32 Mean : 9.602
## L0104 : 2 3rd Qu.: 87.17 3rd Qu.: 0.000
## L0105 : 2 Max. :386.32 Max. :182.390
## (Other):2827 NA's :5
## subsection disk_fresh_weight disk_fresh_volume disk_dry_weight
## Min. :1.000 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.:1.000 1st Qu.: 2.303 1st Qu.: 4.97 1st Qu.: 1.370
## Median :1.000 Median : 4.635 Median : 9.08 Median : 2.875
## Mean :1.457 Mean : 6.095 Mean : 11.53 Mean : 3.763
## 3rd Qu.:2.000 3rd Qu.: 8.258 3rd Qu.: 15.36 3rd Qu.: 5.067
## Max. :2.000 Max. :55.430 Max. :474.00 Max. :35.260
## NA's :6 NA's :7 NA's :6
## disk_dry_volume Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
## Min. : 0.00 Min. : 0.000 Min. : 0.0000
## 1st Qu.: 4.23 1st Qu.: 0.000 1st Qu.: 0.0000
## Median : 7.92 Median : 0.000 Median : 0.0000
## Mean : 11.29 Mean : 1.076 Mean : 0.7675
## 3rd Qu.: 13.54 3rd Qu.: 0.000 3rd Qu.: 0.0000
## Max. :3921.00 Max. :39.560 Max. :35.6400
## NA's :9 NA's :10 NA's :4
## Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest Harvest Time_months
## Min. : 0.0000 Min. : 0.0000 1 :759 : 4
## 1st Qu.: 0.0000 1st Qu.: 0.0000 2 :715 12 months:715
## Median : 0.0000 Median : 0.0000 3 :696 18 months:696
## Mean : 0.5704 Mean : 0.5476 4 :670 24 months:670
## 3rd Qu.: 0.0000 3rd Qu.: 0.0000 NA's: 4 6 months :759
## Max. :16.7900 Max. :30.8600
## NA's :5 NA's :6
## incubation_time block
## 6 :759 1 :727
## 12 :715 2 :713
## 18 :696 3 :718
## 24 :670 4 :682
## NA's: 4 NA's: 4
##
## library(dplyr)
spc_dens<- dat %>% group_by(block,Tag,Harvest,Time_months,incubation_time)%>%
summarize(log_fresh_weight=mean(log_fresh_weight,na.rm = TRUE),
wood_weight=mean(wood_weight,na.rm = TRUE),
disk_fresh_weight = mean(disk_fresh_weight,na.rm = TRUE),
disk_fresh_volume = mean (disk_fresh_volume,na.rm = TRUE),
disk_dry_weight = mean (disk_dry_weight,na.rm = TRUE),
disk_dry_volume= mean (disk_dry_volume,na.rm = TRUE),
Bark_fresh_mass_at_harvest= mean (Bark_fresh_mass_at_harvest,na.rm = TRUE),
Bark_fresh_volume_at_harvest= mean (Bark_fresh_volume_at_harvest,na.rm = TRUE),
Dry_bark_mass_at_harvest= mean (Dry_bark_mass_at_harvest,na.rm = TRUE),
Dry_bark_volume_at_harvest= mean (Dry_bark_volume_at_harvest,na.rm = TRUE),
)
spc_dens## # A tibble: 1,535 × 15
## # Groups: block, Tag, Harvest, Time_months [1,535]
## block Tag Harvest Time_months incubation_time log_fresh_weight wood_weight
## <fct> <fct> <fct> <fct> <fct> <dbl> <dbl>
## 1 1 L0102 1 6 months 6 88.0 0
## 2 1 L0107 3 18 months 18 0 0
## 3 1 L0112 3 18 months 18 83.2 0
## 4 1 L0117 2 12 months 12 50.4 0
## 5 1 L0120 4 24 months 24 55.6 27.3
## 6 1 L0126 2 12 months 12 85.6 0
## 7 1 L0127 4 24 months 24 33.4 19.2
## 8 1 L0129 1 6 months 6 86.2 0
## 9 1 L0154 3 18 months 18 188. 0
## 10 1 L0159 3 18 months 18 54.0 0
## # ℹ 1,525 more rows
## # ℹ 8 more variables: disk_fresh_weight <dbl>, disk_fresh_volume <dbl>,
## # disk_dry_weight <dbl>, disk_dry_volume <dbl>,
## # Bark_fresh_mass_at_harvest <dbl>, Bark_fresh_volume_at_harvest <dbl>,
## # Dry_bark_mass_at_harvest <dbl>, Dry_bark_volume_at_harvest <dbl>spc_dens$bark_mass_fresh<- spc_dens$log_fresh_weight- spc_dens$wood_weight
spc_dens$wood_weight<- spc_dens$wood_weight+0.01
spc_dens$disk_fresh_weight<- spc_dens$disk_fresh_weight+0.1
spc_dens$disk_dry_weight<- spc_dens$disk_dry_weight +0.1
spc_dens$bark_mass_fresh<- spc_dens$bark_mass_fresh+0.1
spc_dens$Bark_fresh_mass_at_harvest<-spc_dens$Bark_fresh_mass_at_harvest+0.1
spc_dens$Dry_bark_mass_at_harvest<- spc_dens$Dry_bark_mass_at_harvest+0.1Calculate the final dry mass of the logs after decomposition using the same formula from Seibold et al., 2021 shown above.
spc_dens$f_wood_mass<- (spc_dens$wood_weight/spc_dens$disk_fresh_weight)*spc_dens$disk_dry_weight
spc_dens$f_bark_mass<- (spc_dens$bark_mass_fresh/spc_dens$Bark_fresh_mass_at_harvest)*spc_dens$Dry_bark_mass_at_harvest
spc_dens$final_mass<- (spc_dens$log_fresh_weight/spc_dens$disk_fresh_weight)*spc_dens$disk_dry_weightFor some WD, we did not have discs (substantially decomposed) so here we will use the fresh weight log as final mass because this was very trivial.
At each harvest we also took photos of each WD. The following figure
S6 
Figure S6 represents
some examples of liana wood of 4.0 cm diameter (a-d) and 2.5 cm diameter
(e-f) at 12 months of decomposition. Inner wood was completely
decomposed but the bark was left intact after the incubation period. The
wood shown in the figure are from species Ayenia grandifolia (a
and e), Senegalia pruinescens (b and d), Urceola rosea
(c), and Piper flaviflorum (f).
library(tidyr)
data_new <- spc_dens # Duplicate data
data_new$final_mass[is.na(data_new$final_mass)] <- data_new$log_fresh_weight[is.na(data_new$final_mass)] # Replace NA values
data_new ## # A tibble: 1,535 × 19
## # Groups: block, Tag, Harvest, Time_months [1,535]
## block Tag Harvest Time_months incubation_time log_fresh_weight wood_weight
## <fct> <fct> <fct> <fct> <fct> <dbl> <dbl>
## 1 1 L0102 1 6 months 6 88.0 0.01
## 2 1 L0107 3 18 months 18 0 0.01
## 3 1 L0112 3 18 months 18 83.2 0.01
## 4 1 L0117 2 12 months 12 50.4 0.01
## 5 1 L0120 4 24 months 24 55.6 27.3
## 6 1 L0126 2 12 months 12 85.6 0.01
## 7 1 L0127 4 24 months 24 33.4 19.2
## 8 1 L0129 1 6 months 6 86.2 0.01
## 9 1 L0154 3 18 months 18 188. 0.01
## 10 1 L0159 3 18 months 18 54.0 0.01
## # ℹ 1,525 more rows
## # ℹ 12 more variables: disk_fresh_weight <dbl>, disk_fresh_volume <dbl>,
## # disk_dry_weight <dbl>, disk_dry_volume <dbl>,
## # Bark_fresh_mass_at_harvest <dbl>, Bark_fresh_volume_at_harvest <dbl>,
## # Dry_bark_mass_at_harvest <dbl>, Dry_bark_volume_at_harvest <dbl>,
## # bark_mass_fresh <dbl>, f_wood_mass <dbl>, f_bark_mass <dbl>,
## # final_mass <dbl>## block Tag Harvest Time_months incubation_time
## 1 :385 T0358 : 2 1 :384 : 4 6 :384
## 2 :383 L0101 : 1 2 :384 12 months:384 12 :384
## 3 :382 L0102 : 1 3 :385 18 months:385 18 :385
## 4 :381 L0103 : 1 4 :378 24 months:378 24 :378
## NA's: 4 L0104 : 1 NA's: 4 6 months :384 NA's: 4
## L0105 : 1
## (Other):1528
## log_fresh_weight wood_weight disk_fresh_weight disk_fresh_volume
## Min. : 0.00 Min. : 0.010 Min. : 0.100 Min. : 0.000
## 1st Qu.: 17.44 1st Qu.: 0.010 1st Qu.: 1.850 1st Qu.: 4.103
## Median : 45.12 Median : 0.010 Median : 4.393 Median : 8.512
## Mean : 58.94 Mean : 8.878 Mean : 5.779 Mean : 10.738
## 3rd Qu.: 83.33 3rd Qu.: 0.010 3rd Qu.: 7.875 3rd Qu.: 14.734
## Max. :386.32 Max. :182.400 Max. :47.450 Max. :238.445
## NA's :5 NA's :5 NA's :5
## disk_dry_weight disk_dry_volume Bark_fresh_mass_at_harvest
## Min. : 0.100 Min. : 0.000 Min. : 0.100
## 1st Qu.: 1.206 1st Qu.: 3.490 1st Qu.: 0.100
## Median : 2.745 Median : 7.425 Median : 0.100
## Mean : 3.612 Mean : 10.510 Mean : 1.096
## 3rd Qu.: 4.942 3rd Qu.: 12.985 3rd Qu.: 0.100
## Max. :28.005 Max. :1972.775 Max. :38.000
## NA's :5 NA's :6
## Bark_fresh_volume_at_harvest Dry_bark_mass_at_harvest
## Min. : 0.0000 Min. : 0.1000
## 1st Qu.: 0.0000 1st Qu.: 0.1000
## Median : 0.0000 Median : 0.1000
## Mean : 0.7094 Mean : 0.6305
## 3rd Qu.: 0.0000 3rd Qu.: 0.1000
## Max. :34.2700 Max. :15.5450
##
## Dry_bark_volume_at_harvest bark_mass_fresh f_wood_mass
## Min. : 0.0000 Min. :-36.67 Min. :1.335e-03
## 1st Qu.: 0.0000 1st Qu.: 10.86 1st Qu.:7.370e-03
## Median : 0.0000 Median : 36.17 Median :8.344e-03
## Mean : 0.5121 Mean : 50.14 Mean :4.021e+00
## 3rd Qu.: 0.0000 3rd Qu.: 70.35 3rd Qu.:1.000e-02
## Max. :30.3150 Max. :386.42 Max. :1.009e+02
## NA's :5 NA's :5
## f_bark_mass final_mass
## Min. :-20.200 Min. : 0.00
## 1st Qu.: 8.403 1st Qu.: 11.21
## Median : 32.120 Median : 28.57
## Mean : 48.395 Mean : 37.03
## 3rd Qu.: 67.765 3rd Qu.: 52.43
## Max. :386.420 Max. :235.69
## NA's :5 NA's :5## [1] 1535 19data_new$Harvest<-as.factor(data_new$Harvest)
data_new$block<-as.factor(data_new$block)
summary(data_new)## block Tag Harvest Time_months incubation_time
## 1 :385 T0358 : 2 1 :384 : 4 6 :384
## 2 :383 L0101 : 1 2 :384 12 months:384 12 :384
## 3 :382 L0102 : 1 3 :385 18 months:385 18 :385
## 4 :381 L0103 : 1 4 :378 24 months:378 24 :378
## NA's: 4 L0104 : 1 NA's: 4 6 months :384 NA's: 4
## L0105 : 1
## (Other):1528
## log_fresh_weight wood_weight disk_fresh_weight disk_fresh_volume
## Min. : 0.00 Min. : 0.010 Min. : 0.100 Min. : 0.000
## 1st Qu.: 17.44 1st Qu.: 0.010 1st Qu.: 1.850 1st Qu.: 4.103
## Median : 45.12 Median : 0.010 Median : 4.393 Median : 8.512
## Mean : 58.94 Mean : 8.878 Mean : 5.779 Mean : 10.738
## 3rd Qu.: 83.33 3rd Qu.: 0.010 3rd Qu.: 7.875 3rd Qu.: 14.734
## Max. :386.32 Max. :182.400 Max. :47.450 Max. :238.445
## NA's :5 NA's :5 NA's :5
## disk_dry_weight disk_dry_volume Bark_fresh_mass_at_harvest
## Min. : 0.100 Min. : 0.000 Min. : 0.100
## 1st Qu.: 1.206 1st Qu.: 3.490 1st Qu.: 0.100
## Median : 2.745 Median : 7.425 Median : 0.100
## Mean : 3.612 Mean : 10.510 Mean : 1.096
## 3rd Qu.: 4.942 3rd Qu.: 12.985 3rd Qu.: 0.100
## Max. :28.005 Max. :1972.775 Max. :38.000
## NA's :5 NA's :6
## Bark_fresh_volume_at_harvest Dry_bark_mass_at_harvest
## Min. : 0.0000 Min. : 0.1000
## 1st Qu.: 0.0000 1st Qu.: 0.1000
## Median : 0.0000 Median : 0.1000
## Mean : 0.7094 Mean : 0.6305
## 3rd Qu.: 0.0000 3rd Qu.: 0.1000
## Max. :34.2700 Max. :15.5450
##
## Dry_bark_volume_at_harvest bark_mass_fresh f_wood_mass
## Min. : 0.0000 Min. :-36.67 Min. :1.335e-03
## 1st Qu.: 0.0000 1st Qu.: 10.86 1st Qu.:7.370e-03
## Median : 0.0000 Median : 36.17 Median :8.344e-03
## Mean : 0.5121 Mean : 50.14 Mean :4.021e+00
## 3rd Qu.: 0.0000 3rd Qu.: 70.35 3rd Qu.:1.000e-02
## Max. :30.3150 Max. :386.42 Max. :1.009e+02
## NA's :5 NA's :5
## f_bark_mass final_mass
## Min. :-20.200 Min. : 0.00
## 1st Qu.: 8.403 1st Qu.: 11.21
## Median : 32.120 Median : 28.57
## Mean : 48.395 Mean : 37.03
## 3rd Qu.: 67.765 3rd Qu.: 52.43
## Max. :386.420 Max. :235.69
## NA's :5 NA's :5## factor()
## 1534 Levels: L0101 L0102 L0103 L0104 L0105 L0106 L0107 L0108 L0109 ... T1182## [1] T0506 L1060
## 1536 Levels: L0101 L0102 L0103 L0104 L0105 L0106 L0107 L0108 L0109 ... T11827.1 Join the harvest data to the initial data file
## species_label species family Tag
## F57 : 65 Eucalyptus citriodora: 65 NA's:1537 T0358 : 2
## 1 : 64 Ayenia grandifolia : 64 L0101 : 1
## 11 : 64 Bauhinia purpurea : 64 L0102 : 1
## 12 : 64 Bridelia stipularis : 64 L0103 : 1
## 18 : 64 Calamus henryanus : 64 L0104 : 1
## 19 : 64 Cayratia trifolia : 64 L0105 : 1
## (Other):1152 (Other) :1152 (Other):1530
## growth_form mesh_size diameter_class diameter_1.size_cm
## Lianas:768 Invertebrates access :768 2.5 cm:786 Min. :1.400
## Trees :769 Invertebrates blocked:769 5.0 cm:751 1st Qu.:2.500
## Median :3.000
## Mean :3.219
## 3rd Qu.:3.800
## Max. :8.300
## NA's :1
## diameter_2.size_cm diameter_3.size_cm Bark.thickness_1._mm
## Min. :1.400 Min. : 1.300 Min. :0.120
## 1st Qu.:2.500 1st Qu.: 2.500 1st Qu.:1.100
## Median :3.100 Median : 3.000 Median :1.560
## Mean :3.229 Mean : 3.267 Mean :1.868
## 3rd Qu.:3.800 3rd Qu.: 3.900 3rd Qu.:2.380
## Max. :8.500 Max. :36.000 Max. :7.430
## NA's :64
## Bark.thickness_2._mm Bark.thickness_3._mm Bark.thickness_4_mm length_cm
## Min. :0.130 Min. :0.110 Min. :0.010 Min. : 1.90
## 1st Qu.:1.040 1st Qu.:1.060 1st Qu.:1.050 1st Qu.: 19.50
## Median :1.500 Median :1.500 Median :1.475 Median : 20.00
## Mean :1.803 Mean :1.823 Mean :1.790 Mean : 20.57
## 3rd Qu.:2.300 3rd Qu.:2.280 3rd Qu.:2.250 3rd Qu.: 20.50
## Max. :8.600 Max. :7.880 Max. :8.130 Max. :205.00
## NA's :64 NA's :64 NA's :65
## Wet_weight_of_wood_block_g Wet_weight_of_small_pieces_of_wood_g
## Min. : 25.04 Min. : 1.45
## 1st Qu.: 71.85 1st Qu.: 8.19
## Median : 117.49 Median :13.32
## Mean : 154.48 Mean :16.79
## 3rd Qu.: 188.28 3rd Qu.:21.98
## Max. :14550.00 Max. :80.30
## NA's :2
## Volume_of_wet_small_pieces.g Dry_weight_of_small_wood_blocks_g
## Min. : 1.70 Min. : 1.670
## 1st Qu.: 11.02 1st Qu.: 4.840
## Median : 17.20 Median : 7.230
## Mean : 20.50 Mean : 8.745
## 3rd Qu.: 26.42 3rd Qu.:11.160
## Max. :151.33 Max. :42.500
## NA's :12
## Dry_volume_of_small_wood_blocks.g av_bark_thickness density
## Min. : 3.32 Min. :0.230 Min. :0.06948
## 1st Qu.: 9.39 1st Qu.:1.104 1st Qu.:0.44807
## Median :14.46 Median :1.516 Median :0.50527
## Mean :16.98 Mean :1.822 Mean :0.52689
## 3rd Qu.:21.92 3rd Qu.:2.297 3rd Qu.:0.58071
## Max. :99.71 Max. :7.145 Max. :1.00422
## NA's :12 NA's :65 NA's :12
## av_wd_diameter wd_under_bark bark_wd_ratio initial_mass
## Min. : 1.400 Min. : 1.281 Min. :0.1698 Min. : 11.46
## 1st Qu.: 2.467 1st Qu.: 2.319 1st Qu.:0.7254 1st Qu.: 41.44
## Median : 3.067 Median : 2.891 Median :1.0854 Median : 62.69
## Mean : 3.242 Mean : 3.043 Mean :1.2171 Mean : 82.67
## 3rd Qu.: 3.867 3rd Qu.: 3.578 3rd Qu.:1.5863 3rd Qu.: 97.86
## Max. :14.667 Max. :14.543 Max. :3.8754 Max. :8065.56
## NA's :65 NA's :65 NA's :14
## Carbon_wood Carbon_bark cellulose_wood cellulose_bark
## Min. :44.23 Min. :40.25 Min. :39.36 Min. :19.84
## 1st Qu.:46.50 1st Qu.:43.30 1st Qu.:46.34 1st Qu.:34.08
## Median :47.50 Median :44.77 Median :49.51 Median :45.15
## Mean :47.10 Mean :45.29 Mean :49.54 Mean :40.80
## 3rd Qu.:47.88 3rd Qu.:47.60 3rd Qu.:55.44 3rd Qu.:47.45
## Max. :50.48 Max. :49.90 Max. :58.87 Max. :56.35
## NA's :128 NA's :128
## Nitrogen_wood Nitrogen_bark hemicellulose_wood hemicellulose_bark
## Min. :0.02365 Min. :0.05325 Min. :10.14 Min. : 8.355
## 1st Qu.:0.05287 1st Qu.:0.07070 1st Qu.:15.65 1st Qu.: 9.930
## Median :0.06540 Median :0.08640 Median :17.05 Median :13.075
## Mean :0.07021 Mean :0.10491 Mean :16.82 Mean :13.021
## 3rd Qu.:0.08220 3rd Qu.:0.13310 3rd Qu.:18.18 3rd Qu.:15.345
## Max. :0.16820 Max. :0.21495 Max. :23.69 Max. :19.290
## NA's :128 NA's :128
## Tannins_wood Tannins_bark ADL_wood ADL_bark
## Min. :0.0350 Min. :0.030 Min. :14.78 Min. : 7.57
## 1st Qu.:0.1000 1st Qu.:0.340 1st Qu.:17.28 1st Qu.:13.40
## Median :0.2625 Median :1.020 Median :18.88 Median :19.34
## Mean :0.6439 Mean :1.480 Mean :20.30 Mean :20.73
## 3rd Qu.:0.6675 3rd Qu.:2.145 3rd Qu.:20.48 3rd Qu.:28.53
## Max. :4.6800 Max. :5.985 Max. :35.68 Max. :37.78
## NA's :192 NA's :128
## Calcium_wood Calcium_bark Potassium_wood Potassium_bark
## Min. : 1.965 Min. : 7.38 Min. : 1.735 Min. : 2.335
## 1st Qu.: 5.100 1st Qu.:21.20 1st Qu.: 3.700 1st Qu.: 4.705
## Median :13.127 Median :33.00 Median : 5.345 Median : 6.270
## Mean :14.452 Mean :35.13 Mean : 7.236 Mean : 7.207
## 3rd Qu.:18.525 3rd Qu.:48.68 3rd Qu.: 9.957 3rd Qu.: 7.975
## Max. :42.422 Max. :71.70 Max. :23.517 Max. :20.383
## NA's :128 NA's :128
## magesium_wood magesium_bark Manganese_wood Manganese_bark
## Min. :0.3400 Min. :0.500 Min. : 8.70 Min. : 11.30
## 1st Qu.:0.6425 1st Qu.:1.175 1st Qu.: 12.18 1st Qu.: 25.25
## Median :1.0400 Median :2.100 Median : 18.55 Median : 76.70
## Mean :1.1255 Mean :1.880 Mean : 85.38 Mean : 202.32
## 3rd Qu.:1.5050 3rd Qu.:2.505 3rd Qu.: 66.08 3rd Qu.: 254.20
## Max. :2.2675 Max. :3.417 Max. :586.92 Max. :1265.15
## NA's :128 NA's :128
## Phosphorous_wood Phosphorous_bark sugars_wood sugars_bark
## Min. :0.2000 Min. :0.390 Min. :0.160 Min. :0.110
## 1st Qu.:0.6150 1st Qu.:0.605 1st Qu.:0.420 1st Qu.:0.420
## Median :0.8075 Median :0.965 Median :0.865 Median :0.990
## Mean :1.2198 Mean :1.190 Mean :1.063 Mean :1.378
## 3rd Qu.:1.3467 3rd Qu.:1.590 3rd Qu.:1.252 3rd Qu.:1.900
## Max. :4.0950 Max. :3.840 Max. :4.530 Max. :6.195
## NA's :128 NA's :128
## Silicon_wood Silicon_bark mean_density density_class
## Min. : 0.1750 Min. : 0.340 Min. :0.4083 Length:1537
## 1st Qu.: 0.5000 1st Qu.: 0.770 1st Qu.:0.4701 Class :character
## Median : 0.5975 Median : 1.655 Median :0.5008 Mode :character
## Mean : 1.6683 Mean : 4.443 Mean :0.5269
## 3rd Qu.: 1.3725 3rd Qu.: 2.685 3rd Qu.:0.5612
## Max. :12.9000 Max. :31.580 Max. :0.8475
## NA's :128
## block Harvest Time_months incubation_time log_fresh_weight
## 1 :385 1 :384 : 4 6 :384 Min. : 0.00
## 2 :383 2 :384 12 months:384 12 :384 1st Qu.: 17.44
## 3 :382 3 :385 18 months:385 18 :385 Median : 45.12
## 4 :381 4 :378 24 months:378 24 :378 Mean : 58.94
## NA's: 6 NA's: 6 6 months :384 NA's: 6 3rd Qu.: 83.33
## NA's : 2 Max. :386.32
## NA's :7
## wood_weight disk_fresh_weight disk_fresh_volume disk_dry_weight
## Min. : 0.010 Min. : 0.100 Min. : 0.000 Min. : 0.100
## 1st Qu.: 0.010 1st Qu.: 1.850 1st Qu.: 4.103 1st Qu.: 1.206
## Median : 0.010 Median : 4.393 Median : 8.512 Median : 2.745
## Mean : 8.878 Mean : 5.779 Mean : 10.738 Mean : 3.612
## 3rd Qu.: 0.010 3rd Qu.: 7.875 3rd Qu.: 14.734 3rd Qu.: 4.942
## Max. :182.400 Max. :47.450 Max. :238.445 Max. :28.005
## NA's :2 NA's :7 NA's :7 NA's :7
## disk_dry_volume Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
## Min. : 0.000 Min. : 0.100 Min. : 0.0000
## 1st Qu.: 3.490 1st Qu.: 0.100 1st Qu.: 0.0000
## Median : 7.425 Median : 0.100 Median : 0.0000
## Mean : 10.510 Mean : 1.096 Mean : 0.7094
## 3rd Qu.: 12.985 3rd Qu.: 0.100 3rd Qu.: 0.0000
## Max. :1972.775 Max. :38.000 Max. :34.2700
## NA's :8 NA's :2 NA's :2
## Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest bark_mass_fresh
## Min. : 0.1000 Min. : 0.0000 Min. :-36.67
## 1st Qu.: 0.1000 1st Qu.: 0.0000 1st Qu.: 10.86
## Median : 0.1000 Median : 0.0000 Median : 36.17
## Mean : 0.6305 Mean : 0.5121 Mean : 50.14
## 3rd Qu.: 0.1000 3rd Qu.: 0.0000 3rd Qu.: 70.35
## Max. :15.5450 Max. :30.3150 Max. :386.42
## NA's :2 NA's :2 NA's :7
## f_wood_mass f_bark_mass final_mass
## Min. :1.335e-03 Min. :-20.200 Min. : 0.00
## 1st Qu.:7.370e-03 1st Qu.: 8.403 1st Qu.: 11.21
## Median :8.344e-03 Median : 32.120 Median : 28.57
## Mean :4.021e+00 Mean : 48.395 Mean : 37.03
## 3rd Qu.:1.000e-02 3rd Qu.: 67.765 3rd Qu.: 52.43
## Max. :1.009e+02 Max. :386.420 Max. :235.69
## NA's :7 NA's :7 NA's :7## species_label species family Tag
## F57 : 65 Eucalyptus citriodora: 65 NA's:1531 T0358 : 2
## 1 : 64 Ayenia grandifolia : 64 L0101 : 1
## 11 : 64 Bauhinia purpurea : 64 L0102 : 1
## 12 : 64 Bridelia stipularis : 64 L0103 : 1
## 18 : 64 Calamus henryanus : 64 L0104 : 1
## 19 : 64 Celastrus sp2 : 64 L0105 : 1
## (Other):1146 (Other) :1146 (Other):1524
## growth_form mesh_size diameter_class diameter_1.size_cm
## Lianas:764 Invertebrates access :764 2.5 cm:782 Min. :1.40
## Trees :767 Invertebrates blocked:767 5.0 cm:749 1st Qu.:2.50
## Median :3.00
## Mean :3.22
## 3rd Qu.:3.80
## Max. :8.30
## NA's :1
## diameter_2.size_cm diameter_3.size_cm Bark.thickness_1._mm
## Min. :1.400 Min. : 1.300 Min. :0.120
## 1st Qu.:2.500 1st Qu.: 2.500 1st Qu.:1.100
## Median :3.100 Median : 3.100 Median :1.560
## Mean :3.229 Mean : 3.267 Mean :1.868
## 3rd Qu.:3.800 3rd Qu.: 3.900 3rd Qu.:2.380
## Max. :8.500 Max. :36.000 Max. :7.430
## NA's :64
## Bark.thickness_2._mm Bark.thickness_3._mm Bark.thickness_4_mm length_cm
## Min. :0.130 Min. :0.110 Min. :0.01 Min. : 1.90
## 1st Qu.:1.040 1st Qu.:1.070 1st Qu.:1.05 1st Qu.: 19.50
## Median :1.500 Median :1.500 Median :1.48 Median : 20.00
## Mean :1.805 Mean :1.824 Mean :1.79 Mean : 20.58
## 3rd Qu.:2.305 3rd Qu.:2.285 3rd Qu.:2.25 3rd Qu.: 20.50
## Max. :8.600 Max. :7.880 Max. :8.13 Max. :205.00
## NA's :64 NA's :64 NA's :65
## Wet_weight_of_wood_block_g Wet_weight_of_small_pieces_of_wood_g
## Min. : 25.04 Min. : 1.45
## 1st Qu.: 71.73 1st Qu.: 8.19
## Median : 117.64 Median :13.34
## Mean : 154.57 Mean :16.79
## 3rd Qu.: 188.16 3rd Qu.:21.98
## Max. :14550.00 Max. :80.30
## NA's :2
## Volume_of_wet_small_pieces.g Dry_weight_of_small_wood_blocks_g
## Min. : 1.70 Min. : 1.670
## 1st Qu.: 10.99 1st Qu.: 4.840
## Median : 17.24 Median : 7.250
## Mean : 20.50 Mean : 8.752
## 3rd Qu.: 26.41 3rd Qu.:11.165
## Max. :151.33 Max. :42.500
## NA's :12
## Dry_volume_of_small_wood_blocks.g av_bark_thickness density
## Min. : 3.32 Min. :0.230 Min. :0.06948
## 1st Qu.: 9.38 1st Qu.:1.105 1st Qu.:0.44810
## Median :14.47 Median :1.518 Median :0.50542
## Mean :16.99 Mean :1.822 Mean :0.52695
## 3rd Qu.:21.92 3rd Qu.:2.297 3rd Qu.:0.58077
## Max. :99.71 Max. :7.145 Max. :1.00422
## NA's :12 NA's :65 NA's :12
## av_wd_diameter wd_under_bark bark_wd_ratio initial_mass
## Min. : 1.400 Min. : 1.281 Min. :0.1698 Min. : 11.46
## 1st Qu.: 2.467 1st Qu.: 2.318 1st Qu.:0.7254 1st Qu.: 41.43
## Median : 3.067 Median : 2.893 Median :1.0868 Median : 63.00
## Mean : 3.242 Mean : 3.043 Mean :1.2175 Mean : 82.77
## 3rd Qu.: 3.867 3rd Qu.: 3.577 3rd Qu.:1.5837 3rd Qu.: 98.15
## Max. :14.667 Max. :14.543 Max. :3.8754 Max. :8065.56
## NA's :65 NA's :65 NA's :14
## Carbon_wood Carbon_bark cellulose_wood cellulose_bark
## Min. :44.23 Min. :40.25 Min. :39.36 Min. :19.84
## 1st Qu.:46.50 1st Qu.:43.30 1st Qu.:46.34 1st Qu.:34.08
## Median :47.60 Median :44.77 Median :49.51 Median :45.15
## Mean :47.10 Mean :45.27 Mean :49.56 Mean :40.81
## 3rd Qu.:47.91 3rd Qu.:47.60 3rd Qu.:55.44 3rd Qu.:47.45
## Max. :50.48 Max. :49.90 Max. :58.87 Max. :56.35
## NA's :128 NA's :128
## Nitrogen_wood Nitrogen_bark hemicellulose_wood hemicellulose_bark
## Min. :0.02365 Min. :0.05325 Min. :10.14 Min. : 8.355
## 1st Qu.:0.05287 1st Qu.:0.07070 1st Qu.:15.65 1st Qu.: 9.930
## Median :0.06540 Median :0.09260 Median :17.05 Median :13.075
## Mean :0.07015 Mean :0.10493 Mean :16.82 Mean :13.021
## 3rd Qu.:0.08220 3rd Qu.:0.13310 3rd Qu.:18.44 3rd Qu.:15.345
## Max. :0.16820 Max. :0.21495 Max. :23.69 Max. :19.290
## NA's :128 NA's :128
## Tannins_wood Tannins_bark ADL_wood ADL_bark
## Min. :0.0350 Min. :0.030 Min. :14.78 Min. : 7.57
## 1st Qu.:0.1000 1st Qu.:0.340 1st Qu.:17.17 1st Qu.:13.40
## Median :0.2625 Median :1.020 Median :18.88 Median :19.34
## Mean :0.6439 Mean :1.483 Mean :20.29 Mean :20.69
## 3rd Qu.:0.6238 3rd Qu.:2.145 3rd Qu.:20.48 3rd Qu.:28.53
## Max. :4.6800 Max. :5.985 Max. :35.68 Max. :37.78
## NA's :192 NA's :128
## Calcium_wood Calcium_bark Potassium_wood Potassium_bark
## Min. : 1.965 Min. : 7.38 Min. : 1.735 Min. : 2.335
## 1st Qu.: 4.840 1st Qu.:21.20 1st Qu.: 3.933 1st Qu.: 4.705
## Median :13.127 Median :34.24 Median : 5.345 Median : 6.305
## Mean :14.438 Mean :35.20 Mean : 7.220 Mean : 7.208
## 3rd Qu.:18.525 3rd Qu.:48.68 3rd Qu.: 9.957 3rd Qu.: 7.975
## Max. :42.422 Max. :71.70 Max. :23.517 Max. :20.383
## NA's :128 NA's :128
## magesium_wood magesium_bark Manganese_wood Manganese_bark
## Min. :0.3400 Min. :0.500 Min. : 8.70 Min. : 11.30
## 1st Qu.:0.6425 1st Qu.:1.175 1st Qu.: 12.18 1st Qu.: 25.25
## Median :1.0400 Median :2.100 Median : 16.82 Median : 76.70
## Mean :1.1245 Mean :1.882 Mean : 85.39 Mean : 202.51
## 3rd Qu.:1.5050 3rd Qu.:2.505 3rd Qu.: 66.08 3rd Qu.: 254.20
## Max. :2.2675 Max. :3.417 Max. :586.92 Max. :1265.15
## NA's :128 NA's :128
## Phosphorous_wood Phosphorous_bark sugars_wood sugars_bark
## Min. :0.2000 Min. :0.390 Min. :0.1600 Min. :0.110
## 1st Qu.:0.6150 1st Qu.:0.605 1st Qu.:0.3937 1st Qu.:0.420
## Median :0.8075 Median :0.965 Median :0.9200 Median :0.990
## Mean :1.2183 Mean :1.191 Mean :1.0647 Mean :1.380
## 3rd Qu.:1.3467 3rd Qu.:1.590 3rd Qu.:1.4975 3rd Qu.:1.900
## Max. :4.0950 Max. :3.840 Max. :4.5300 Max. :6.195
## NA's :128 NA's :128
## Silicon_wood Silicon_bark mean_density density_class block
## Min. : 0.1750 Min. : 0.340 Min. :0.4083 Length:1531 1:385
## 1st Qu.: 0.5000 1st Qu.: 0.770 1st Qu.:0.4657 Class :character 2:383
## Median : 0.5975 Median : 1.580 Median :0.5008 Mode :character 3:382
## Mean : 1.6684 Mean : 4.436 Mean :0.5269 4:381
## 3rd Qu.: 1.3725 3rd Qu.: 2.685 3rd Qu.:0.5580
## Max. :12.9000 Max. :31.580 Max. :0.8475
## NA's :128
## Harvest Time_months incubation_time log_fresh_weight wood_weight
## 1:384 : 0 6 :384 Min. : 0.00 Min. : 0.010
## 2:384 12 months:384 12:384 1st Qu.: 17.87 1st Qu.: 0.010
## 3:385 18 months:385 18:385 Median : 45.30 Median : 0.010
## 4:378 24 months:378 24:378 Mean : 59.10 Mean : 8.901
## 6 months :384 3rd Qu.: 83.49 3rd Qu.: 0.010
## Max. :386.32 Max. :182.400
## NA's :5
## disk_fresh_weight disk_fresh_volume disk_dry_weight disk_dry_volume
## Min. : 0.100 Min. : 0.000 Min. : 0.100 Min. : 0.000
## 1st Qu.: 1.881 1st Qu.: 4.206 1st Qu.: 1.218 1st Qu.: 3.520
## Median : 4.410 Median : 8.523 Median : 2.745 Median : 7.425
## Mean : 5.793 Mean : 10.767 Mean : 3.621 Mean : 10.538
## 3rd Qu.: 7.875 3rd Qu.: 14.773 3rd Qu.: 4.945 3rd Qu.: 13.010
## Max. :47.450 Max. :238.445 Max. :28.005 Max. :1972.775
## NA's :5 NA's :5 NA's :5 NA's :6
## Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
## Min. : 0.100 Min. : 0.0000
## 1st Qu.: 0.100 1st Qu.: 0.0000
## Median : 0.100 Median : 0.0000
## Mean : 1.099 Mean : 0.7113
## 3rd Qu.: 0.100 3rd Qu.: 0.0000
## Max. :38.000 Max. :34.2700
##
## Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest bark_mass_fresh
## Min. : 0.1000 Min. : 0.0000 Min. :-36.67
## 1st Qu.: 0.1000 1st Qu.: 0.0000 1st Qu.: 10.92
## Median : 0.1000 Median : 0.0000 Median : 36.24
## Mean : 0.6319 Mean : 0.5135 Mean : 50.28
## 3rd Qu.: 0.1000 3rd Qu.: 0.0000 3rd Qu.: 70.51
## Max. :15.5450 Max. :30.3150 Max. :386.42
## NA's :5
## f_wood_mass f_bark_mass final_mass
## Min. :1.335e-03 Min. :-20.200 Min. : 0.00
## 1st Qu.:7.362e-03 1st Qu.: 8.466 1st Qu.: 11.37
## Median :8.342e-03 Median : 32.355 Median : 28.64
## Mean :4.032e+00 Mean : 48.522 Mean : 37.13
## 3rd Qu.:1.000e-02 3rd Qu.: 67.785 3rd Qu.: 52.45
## Max. :1.009e+02 Max. :386.420 Max. :235.69
## NA's :5 NA's :5 NA's :5## Ayenia grandifolia Bauhinia purpurea Bridelia stipularis
## 64 64 64
## Calamus henryanus Cayratia trifolia Celastrus sp1
## 64 62 63
## Celastrus sp2 Cheniella touranensis Cunninghamia lanceolata
## 64 64 63
## Eucalyptus citriodora Ficus altissima Iodes vitiginea
## 65 64 64
## Kleinhovia hospita Macaranga denticulata Mesua ferrea
## 64 64 64
## Piper flaviflorum Piper umbellatum Senegalia pruinescens
## 63 64 64
## Shorea assamica Tectona grandis Thyrsostachys siamensis
## 63 64 64
## Toona ciliata Urceola rosea Ventilago leiocarpa
## 64 64 64hav_datb$mesh_size<- as.factor(hav_datb$mesh_size)
hav_datb$Harvest<- as.factor(hav_datb$Harvest)
hav_datb$Time_months<- as.factor(hav_datb$Time_months)
hav_datb$incubation_time<- as.factor(hav_datb$incubation_time)Calculate percentage mass loss percentage mass loss was calculated using the equation mentioned above and used here as per_ml:
hav_datb$per_ml<- ((hav_datb$initial_mass-hav_datb$final_mass)/hav_datb$initial_mass)*100
summary(hav_datb)## species_label species family Tag
## F57 : 65 Eucalyptus citriodora: 65 NA's:1531 T0358 : 2
## 1 : 64 Ayenia grandifolia : 64 L0101 : 1
## 11 : 64 Bauhinia purpurea : 64 L0102 : 1
## 12 : 64 Bridelia stipularis : 64 L0103 : 1
## 18 : 64 Calamus henryanus : 64 L0104 : 1
## 19 : 64 Celastrus sp2 : 64 L0105 : 1
## (Other):1146 (Other) :1146 (Other):1524
## growth_form mesh_size diameter_class diameter_1.size_cm
## Lianas:764 Invertebrates access :764 2.5 cm:782 Min. :1.40
## Trees :767 Invertebrates blocked:767 5.0 cm:749 1st Qu.:2.50
## Median :3.00
## Mean :3.22
## 3rd Qu.:3.80
## Max. :8.30
## NA's :1
## diameter_2.size_cm diameter_3.size_cm Bark.thickness_1._mm
## Min. :1.400 Min. : 1.300 Min. :0.120
## 1st Qu.:2.500 1st Qu.: 2.500 1st Qu.:1.100
## Median :3.100 Median : 3.100 Median :1.560
## Mean :3.229 Mean : 3.267 Mean :1.868
## 3rd Qu.:3.800 3rd Qu.: 3.900 3rd Qu.:2.380
## Max. :8.500 Max. :36.000 Max. :7.430
## NA's :64
## Bark.thickness_2._mm Bark.thickness_3._mm Bark.thickness_4_mm length_cm
## Min. :0.130 Min. :0.110 Min. :0.01 Min. : 1.90
## 1st Qu.:1.040 1st Qu.:1.070 1st Qu.:1.05 1st Qu.: 19.50
## Median :1.500 Median :1.500 Median :1.48 Median : 20.00
## Mean :1.805 Mean :1.824 Mean :1.79 Mean : 20.58
## 3rd Qu.:2.305 3rd Qu.:2.285 3rd Qu.:2.25 3rd Qu.: 20.50
## Max. :8.600 Max. :7.880 Max. :8.13 Max. :205.00
## NA's :64 NA's :64 NA's :65
## Wet_weight_of_wood_block_g Wet_weight_of_small_pieces_of_wood_g
## Min. : 25.04 Min. : 1.45
## 1st Qu.: 71.73 1st Qu.: 8.19
## Median : 117.64 Median :13.34
## Mean : 154.57 Mean :16.79
## 3rd Qu.: 188.16 3rd Qu.:21.98
## Max. :14550.00 Max. :80.30
## NA's :2
## Volume_of_wet_small_pieces.g Dry_weight_of_small_wood_blocks_g
## Min. : 1.70 Min. : 1.670
## 1st Qu.: 10.99 1st Qu.: 4.840
## Median : 17.24 Median : 7.250
## Mean : 20.50 Mean : 8.752
## 3rd Qu.: 26.41 3rd Qu.:11.165
## Max. :151.33 Max. :42.500
## NA's :12
## Dry_volume_of_small_wood_blocks.g av_bark_thickness density
## Min. : 3.32 Min. :0.230 Min. :0.06948
## 1st Qu.: 9.38 1st Qu.:1.105 1st Qu.:0.44810
## Median :14.47 Median :1.518 Median :0.50542
## Mean :16.99 Mean :1.822 Mean :0.52695
## 3rd Qu.:21.92 3rd Qu.:2.297 3rd Qu.:0.58077
## Max. :99.71 Max. :7.145 Max. :1.00422
## NA's :12 NA's :65 NA's :12
## av_wd_diameter wd_under_bark bark_wd_ratio initial_mass
## Min. : 1.400 Min. : 1.281 Min. :0.1698 Min. : 11.46
## 1st Qu.: 2.467 1st Qu.: 2.318 1st Qu.:0.7254 1st Qu.: 41.43
## Median : 3.067 Median : 2.893 Median :1.0868 Median : 63.00
## Mean : 3.242 Mean : 3.043 Mean :1.2175 Mean : 82.77
## 3rd Qu.: 3.867 3rd Qu.: 3.577 3rd Qu.:1.5837 3rd Qu.: 98.15
## Max. :14.667 Max. :14.543 Max. :3.8754 Max. :8065.56
## NA's :65 NA's :65 NA's :14
## Carbon_wood Carbon_bark cellulose_wood cellulose_bark
## Min. :44.23 Min. :40.25 Min. :39.36 Min. :19.84
## 1st Qu.:46.50 1st Qu.:43.30 1st Qu.:46.34 1st Qu.:34.08
## Median :47.60 Median :44.77 Median :49.51 Median :45.15
## Mean :47.10 Mean :45.27 Mean :49.56 Mean :40.81
## 3rd Qu.:47.91 3rd Qu.:47.60 3rd Qu.:55.44 3rd Qu.:47.45
## Max. :50.48 Max. :49.90 Max. :58.87 Max. :56.35
## NA's :128 NA's :128
## Nitrogen_wood Nitrogen_bark hemicellulose_wood hemicellulose_bark
## Min. :0.02365 Min. :0.05325 Min. :10.14 Min. : 8.355
## 1st Qu.:0.05287 1st Qu.:0.07070 1st Qu.:15.65 1st Qu.: 9.930
## Median :0.06540 Median :0.09260 Median :17.05 Median :13.075
## Mean :0.07015 Mean :0.10493 Mean :16.82 Mean :13.021
## 3rd Qu.:0.08220 3rd Qu.:0.13310 3rd Qu.:18.44 3rd Qu.:15.345
## Max. :0.16820 Max. :0.21495 Max. :23.69 Max. :19.290
## NA's :128 NA's :128
## Tannins_wood Tannins_bark ADL_wood ADL_bark
## Min. :0.0350 Min. :0.030 Min. :14.78 Min. : 7.57
## 1st Qu.:0.1000 1st Qu.:0.340 1st Qu.:17.17 1st Qu.:13.40
## Median :0.2625 Median :1.020 Median :18.88 Median :19.34
## Mean :0.6439 Mean :1.483 Mean :20.29 Mean :20.69
## 3rd Qu.:0.6238 3rd Qu.:2.145 3rd Qu.:20.48 3rd Qu.:28.53
## Max. :4.6800 Max. :5.985 Max. :35.68 Max. :37.78
## NA's :192 NA's :128
## Calcium_wood Calcium_bark Potassium_wood Potassium_bark
## Min. : 1.965 Min. : 7.38 Min. : 1.735 Min. : 2.335
## 1st Qu.: 4.840 1st Qu.:21.20 1st Qu.: 3.933 1st Qu.: 4.705
## Median :13.127 Median :34.24 Median : 5.345 Median : 6.305
## Mean :14.438 Mean :35.20 Mean : 7.220 Mean : 7.208
## 3rd Qu.:18.525 3rd Qu.:48.68 3rd Qu.: 9.957 3rd Qu.: 7.975
## Max. :42.422 Max. :71.70 Max. :23.517 Max. :20.383
## NA's :128 NA's :128
## magesium_wood magesium_bark Manganese_wood Manganese_bark
## Min. :0.3400 Min. :0.500 Min. : 8.70 Min. : 11.30
## 1st Qu.:0.6425 1st Qu.:1.175 1st Qu.: 12.18 1st Qu.: 25.25
## Median :1.0400 Median :2.100 Median : 16.82 Median : 76.70
## Mean :1.1245 Mean :1.882 Mean : 85.39 Mean : 202.51
## 3rd Qu.:1.5050 3rd Qu.:2.505 3rd Qu.: 66.08 3rd Qu.: 254.20
## Max. :2.2675 Max. :3.417 Max. :586.92 Max. :1265.15
## NA's :128 NA's :128
## Phosphorous_wood Phosphorous_bark sugars_wood sugars_bark
## Min. :0.2000 Min. :0.390 Min. :0.1600 Min. :0.110
## 1st Qu.:0.6150 1st Qu.:0.605 1st Qu.:0.3937 1st Qu.:0.420
## Median :0.8075 Median :0.965 Median :0.9200 Median :0.990
## Mean :1.2183 Mean :1.191 Mean :1.0647 Mean :1.380
## 3rd Qu.:1.3467 3rd Qu.:1.590 3rd Qu.:1.4975 3rd Qu.:1.900
## Max. :4.0950 Max. :3.840 Max. :4.5300 Max. :6.195
## NA's :128 NA's :128
## Silicon_wood Silicon_bark mean_density density_class block
## Min. : 0.1750 Min. : 0.340 Min. :0.4083 Length:1531 1:385
## 1st Qu.: 0.5000 1st Qu.: 0.770 1st Qu.:0.4657 Class :character 2:383
## Median : 0.5975 Median : 1.580 Median :0.5008 Mode :character 3:382
## Mean : 1.6684 Mean : 4.436 Mean :0.5269 4:381
## 3rd Qu.: 1.3725 3rd Qu.: 2.685 3rd Qu.:0.5580
## Max. :12.9000 Max. :31.580 Max. :0.8475
## NA's :128
## Harvest Time_months incubation_time log_fresh_weight wood_weight
## 1:384 : 0 6 :384 Min. : 0.00 Min. : 0.010
## 2:384 12 months:384 12:384 1st Qu.: 17.87 1st Qu.: 0.010
## 3:385 18 months:385 18:385 Median : 45.30 Median : 0.010
## 4:378 24 months:378 24:378 Mean : 59.10 Mean : 8.901
## 6 months :384 3rd Qu.: 83.49 3rd Qu.: 0.010
## Max. :386.32 Max. :182.400
## NA's :5
## disk_fresh_weight disk_fresh_volume disk_dry_weight disk_dry_volume
## Min. : 0.100 Min. : 0.000 Min. : 0.100 Min. : 0.000
## 1st Qu.: 1.881 1st Qu.: 4.206 1st Qu.: 1.218 1st Qu.: 3.520
## Median : 4.410 Median : 8.523 Median : 2.745 Median : 7.425
## Mean : 5.793 Mean : 10.767 Mean : 3.621 Mean : 10.538
## 3rd Qu.: 7.875 3rd Qu.: 14.773 3rd Qu.: 4.945 3rd Qu.: 13.010
## Max. :47.450 Max. :238.445 Max. :28.005 Max. :1972.775
## NA's :5 NA's :5 NA's :5 NA's :6
## Bark_fresh_mass_at_harvest Bark_fresh_volume_at_harvest
## Min. : 0.100 Min. : 0.0000
## 1st Qu.: 0.100 1st Qu.: 0.0000
## Median : 0.100 Median : 0.0000
## Mean : 1.099 Mean : 0.7113
## 3rd Qu.: 0.100 3rd Qu.: 0.0000
## Max. :38.000 Max. :34.2700
##
## Dry_bark_mass_at_harvest Dry_bark_volume_at_harvest bark_mass_fresh
## Min. : 0.1000 Min. : 0.0000 Min. :-36.67
## 1st Qu.: 0.1000 1st Qu.: 0.0000 1st Qu.: 10.92
## Median : 0.1000 Median : 0.0000 Median : 36.24
## Mean : 0.6319 Mean : 0.5135 Mean : 50.28
## 3rd Qu.: 0.1000 3rd Qu.: 0.0000 3rd Qu.: 70.51
## Max. :15.5450 Max. :30.3150 Max. :386.42
## NA's :5
## f_wood_mass f_bark_mass final_mass per_ml
## Min. :1.335e-03 Min. :-20.200 Min. : 0.00 Min. :-52.24
## 1st Qu.:7.362e-03 1st Qu.: 8.466 1st Qu.: 11.37 1st Qu.: 26.64
## Median :8.342e-03 Median : 32.355 Median : 28.64 Median : 49.40
## Mean :4.032e+00 Mean : 48.522 Mean : 37.13 Mean : 51.10
## 3rd Qu.:1.000e-02 3rd Qu.: 67.785 3rd Qu.: 52.45 3rd Qu.: 78.26
## Max. :1.009e+02 Max. :386.420 Max. :235.69 Max. :100.00
## NA's :5 NA's :5 NA's :5 NA's :19## [1] 1531 73## Ayenia grandifolia Bauhinia purpurea Bridelia stipularis
## 64 64 64
## Calamus henryanus Cayratia trifolia Celastrus sp1
## 64 62 63
## Celastrus sp2 Cheniella touranensis Cunninghamia lanceolata
## 64 64 63
## Eucalyptus citriodora Ficus altissima Iodes vitiginea
## 65 64 64
## Kleinhovia hospita Macaranga denticulata Mesua ferrea
## 64 64 64
## Piper flaviflorum Piper umbellatum Senegalia pruinescens
## 63 64 64
## Shorea assamica Tectona grandis Thyrsostachys siamensis
## 63 64 64
## Toona ciliata Urceola rosea Ventilago leiocarpa
## 64 64 64spc_dens2<- hav_datb%>% group_by( Harvest,block, species,growth_form,mesh_size ,diameter_class, Tag)%>%
summarize(mean(per_ml))
dim(spc_dens2)## [1] 1531 8## [1] 1531 73## [1] 1427 73## # A tibble: 384 × 5
## # Groups: growth_form, Harvest, block [32]
## growth_form Harvest block species n
## <fct> <fct> <fct> <fct> <int>
## 1 Lianas 1 1 Ayenia grandifolia 4
## 2 Lianas 1 1 Bridelia stipularis 4
## 3 Lianas 1 1 Calamus henryanus 4
## 4 Lianas 1 1 Cayratia trifolia 4
## 5 Lianas 1 1 Celastrus sp1 4
## 6 Lianas 1 1 Celastrus sp2 4
## 7 Lianas 1 1 Cheniella touranensis 4
## 8 Lianas 1 1 Iodes vitiginea 4
## 9 Lianas 1 1 Piper flaviflorum 4
## 10 Lianas 1 1 Senegalia pruinescens 4
## # ℹ 374 more rows8 Import the phylogeny tree
Here we need to plot the phylogeny tree of the species we use in our experiment.
#ggtree(trb)+ geom_tiplab()
species_order<- c("Bridelia stipularis","Mesua ferrea","Macaranga denticulata","Celastrus sp1","Celastrus sp2","Bauhinia purpurea","Senegalia pruinescens",
"Cheniella touranensis", "Ventilago leiocarpa", "Ficus altissima","Kleinhovia hospita","Ayenia grandifolia","Shorea assamica","Toona ciliata",
"Eucalyptus citriodora","Cayratia trifolia","Urceola rosea","Tectona grandis","Iodes vitiginea","Calamus henryanus","Thyrsostachys siamensis",
"Piper umbellatum","Piper flaviflorum","Cunninghamia lanceolata")9 Average mass loss in every collection month across species, mesh size and diameter class
massloss<- hav_datb %>% group_by(species, diameter_class,mesh_size, incubation_time,growth_form)%>%
summarize(meanmass=mean(per_ml,na.rm = TRUE),
se=sd(per_ml,na.rm = TRUE)/sqrt(n()),
)massloss$species<-factor(massloss$species,levels = species_order)
massloss$species<- fct_rev(massloss$species)
summary(massloss)## species diameter_class mesh_size
## Cunninghamia lanceolata: 16 2.5 cm:192 Invertebrates access :192
## Piper flaviflorum : 16 5.0 cm:192 Invertebrates blocked:192
## Piper umbellatum : 16
## Thyrsostachys siamensis: 16
## Calamus henryanus : 16
## Iodes vitiginea : 16
## (Other) :288
## incubation_time growth_form meanmass se
## 6 :96 Lianas:192 Min. :-15.07 Min. : 0.000
## 12:96 Trees :192 1st Qu.: 30.13 1st Qu.: 5.257
## 18:96 Median : 50.32 Median : 9.195
## 24:96 Mean : 50.81 Mean : 9.717
## 3rd Qu.: 71.25 3rd Qu.:13.045
## Max. :100.00 Max. :28.421
## NA's :1coarse_k<- massloss[massloss$mesh_size %in% c("Invertebrates access"), ]
fine_k<- massloss[massloss$mesh_size %in% c("Invertebrates blocked"), ]
fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_6<- fine_small[fine_small$incubation_time %in% c("6"), ]
plot(trb)
meta_data <- data.frame(ID = fine_small_6$species,
group = fine_small_6$growth_form)
xm<- meta_data %>%
mutate(ID = str_replace(ID, " ", "_"))
x <- full_join(as_tibble(trb), xm, by = c("label" = "ID"))
x## # A tbl_tree abstraction: 47 × 5
## # which can be converted to treedata or phylo
## # via as.treedata or as.phylo
## parent node branch.length label group
## <int> <int> <dbl> <chr> <fct>
## 1 30 1 89.8 Tectona_grandis Trees
## 2 30 2 89.8 Urceola_rosea Lianas
## 3 29 3 102. Iodes_vitiginea Lianas
## 4 35 4 84.8 Cheniella_touranensis Lianas
## 5 36 5 84.0 Senegalia_pruinescens Lianas
## 6 36 6 84.0 Bauhinia_purpurea Trees
## 7 37 7 85.5 Ficus_altissima Trees
## 8 37 8 85.5 Ventilago_leiocarpa Lianas
## 9 40 9 94.9 Mesua_ferrea Trees
## 10 40 10 94.9 Bridelia_stipularis Lianas
## # ℹ 37 more rowslibrary(treeio)
tree2 <- as.treedata(x)
xt<- ggtree(tree2) + geom_tiplab(aes(color = group,fontface="bold.italic",vjust=0))+xlim(0,420)+
theme(legend.position = c(0.2,0.9)) + labs(colour= "Growth form")+
theme(legend.direction = "vertical", legend.box = "vertical",
legend.text=element_text(size=12,face="bold"),
legend.background = element_blank(),
legend.key.width=unit(1,"cm"),
legend.key.height=unit(1,"cm"),
legend.title = element_text(size=12, face= "bold"))+
geom_text (aes (280, 24.3), label = 'Phyllanthaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (280, 23.3), label = 'Calophyllaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (280, 22.3), label = 'Euphorbiaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (280, 20.9), label = 'Celastraceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (290, 19.3), label = 'Fabaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (280, 16.3), label = 'Rhamnaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (280, 15.3), label = 'Moraceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (280, 13.9), label = 'Malvaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (290, 12.3), label = 'Dipterocarpaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (300, 11.3), label = 'Meliaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (300, 10.3), label = 'Myrtaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (300, 9.3), label = 'Vitaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (280, 8.3), label = 'Apocynaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (280, 7.3), label = 'Lamiaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (280, 6.3), label = 'Icacinaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (260, 5.3), label = 'Arecaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (260, 4.3), label = 'Poaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (260, 2.8), label = 'Piperaceae', check_overlap = TRUE, color = 'black', size = 3)+
geom_text (aes (260, 1.3), label = 'Cupressaceae', check_overlap = TRUE, color = 'black', size = 3)
xt
Figure S1 Phylogenetic tree showing the relationship between liana and tree species selected for this experiment
### Phylogenetic tree showing the relationship between liana and tree species selected for this experiment.
ggsave(filename="Figure S1.png", plot=xt, device="png",
path=path, height=6, width=8, units="in", dpi=500)fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_6<- fine_small[fine_small$incubation_time %in% c("6"), ]
sm_fn6<- ggplot(fine_small_6, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= fine_small_6$meanmass-fine_small_6$se, xmax=fine_small_6$meanmass+fine_small_6$se, width=0.5)+ scale_x_continuous(limits=c(-30,60))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_text(size=9, face= "italic") ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+
labs(x= "Percentage mass loss",fill= "Growth form")+ theme(legend.position = c(0.2,0.7),legend.background = element_blank())+
ggtitle(label="2.5 cm wood- inverterbrates absent", subtitle ="6 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_fn6
fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_12<- fine_small[fine_small$incubation_time %in% c("12"), ]
sm_fn12<- ggplot(fine_small_12, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= fine_small_12$meanmass-fine_small_12$se, xmax=fine_small_12$meanmass+fine_small_12$se, width=0.5)+
scale_x_continuous(limits=c(-15,60))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=9,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+
labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="",subtitle ="12 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_fn12
fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_18<- fine_small[fine_small$incubation_time %in% c("18"), ]
sm_fn18<- ggplot(fine_small_18, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= fine_small_18$meanmass-fine_small_18$se, xmax=fine_small_18$meanmass+fine_small_18$se, width=0.5)+
scale_x_continuous(limits=c(-5,90))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=9,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="18 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_fn18
fine_small<- fine_k[fine_k$diameter_class %in% c("2.5 cm"), ]
fine_small_24<- fine_small[fine_small$incubation_time %in% c("24"), ]
sm_fn24<- ggplot(fine_small_24, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= fine_small_24$meanmass-fine_small_24$se, xmax=fine_small_24$meanmass+fine_small_24$se, width=0.5)+
scale_x_continuous(limits=c(0,90))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=9,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="24 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_fn24fine_big<- fine_k[fine_k$diameter_class %in% c("5.0 cm"), ]
fine_big_6<- fine_big[fine_big$incubation_time %in% c("6"), ]
bg_fn6<- ggplot(fine_big_6, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= fine_big_6$meanmass-fine_big_6$se, xmax=fine_big_6$meanmass+fine_big_6$se, width=0.5)+ scale_x_continuous(limits=c(-30,60))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_text(size=9, face= "italic") ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+
labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label= "
4.0 cm - Inverterbrates absent", subtitle ="6 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_fn6
fine_big<- fine_k[fine_k$diameter_class %in% c("5.0 cm"), ]
fine_big_12<- fine_big[fine_big$incubation_time %in% c("12"), ]
bg_fn12<- ggplot(fine_big_12, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= fine_big_12$meanmass-fine_big_12$se, xmax=fine_big_12$meanmass+fine_big_12$se, width=0.5)+ scale_x_continuous(limits=c(-15,60))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="12 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_fn12
fine_big<- fine_k[fine_k$diameter_class %in% c("5.0 cm"), ]
fine_big_18<- fine_big[fine_big$incubation_time %in% c("18"), ]
bg_fn18<- ggplot(fine_big_18, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= fine_big_18$meanmass-fine_big_18$se, xmax=fine_big_18$meanmass+fine_big_18$se, width=0.5)+ scale_x_continuous(limits=c(-5,90))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=9,,colour="black"),
axis.title.x =element_text(size=9, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="",subtitle ="18 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_fn18
fine_big<- fine_k[fine_k$diameter_class %in% c("5.0 cm"), ]
fine_big_24<- fine_big[fine_big$incubation_time %in% c("24"), ]
bg_fn24<- ggplot(fine_big_24, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= fine_big_24$meanmass-fine_big_24$se, xmax=fine_big_24$meanmass+fine_big_24$se, width=0.5)+ scale_x_continuous(limits=c(0,90))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=9,colour="black"),
axis.title.x =element_text(size=9, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="",subtitle ="24 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_fn24#FigS1<- sm_fn6+ sm_fn12+sm_fn18+sm_fn24+bg_fn6+bg_fn12+bg_fn18+bg_fn24+ plot_layout(ncol = 4)
#FigS1#ggsave(filename="FigS1.png", plot=FigS1, device="png",
# path=path, height=9, width=16, units="in", dpi=500)coarse_small<- coarse_k[coarse_k$diameter_class %in% c("2.5 cm"), ]
coarse_small_6<- coarse_small[coarse_small$incubation_time %in% c("6"), ]
sm_crs6<- ggplot(coarse_small_6, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= coarse_small_6$meanmass-coarse_small_6$se,xmax=coarse_small_6$meanmass+coarse_small_6$se, width=0.5)+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_text(size=9,face="italic") ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="2.5 cm wood - inverterbrates present", subtitle ="6 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_crs6
coarse_small<- coarse_k[coarse_k$diameter_class %in% c("2.5 cm"), ]
coarse_small_12<- coarse_small[coarse_small$incubation_time %in% c("12"), ]
sm_crs12<- ggplot(coarse_small_12, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= coarse_small_12$meanmass-coarse_small_12$se,xmax=coarse_small_12$meanmass+coarse_small_12$se, width=0.5)+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="12 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_crs12
coarse_small_18<- coarse_small[coarse_small$incubation_time %in% c("18"), ]
sm_crs18<- ggplot(coarse_small_18, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= coarse_small_18$meanmass-coarse_small_18$se,xmax=coarse_small_18$meanmass+coarse_small_18$se, width=0.5)+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="18 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_crs18
coarse_small_24<- coarse_small[coarse_small$incubation_time %in% c("24"), ]
sm_crs24<- ggplot(coarse_small_24, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= coarse_small_24$meanmass-coarse_small_24$se,xmax=coarse_small_24$meanmass+coarse_small_24$se, width=0.5)+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="24 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#sm_crs24coarse_big<- coarse_k[coarse_k$diameter_class %in% c("5.0 cm"), ]
coarse_big_6<- coarse_big[coarse_big$incubation_time %in% c("6"), ]
bg_crs6<- ggplot(coarse_big_6, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= coarse_big_6$meanmass-coarse_big_6$se,xmax=coarse_big_6$meanmass+coarse_big_6$se, width=0.5)+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_text(size = 9, face = "italic") ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="4.0 cm wood - inverterbrates present", subtitle =" 6 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_crs6
coarse_big<- coarse_k[coarse_k$diameter_class %in% c("5.0 cm"), ]
coarse_big_12<- coarse_big[coarse_big$incubation_time %in% c("12"), ]
bg_crs12<- ggplot(coarse_big_12, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= coarse_big_12$meanmass-coarse_big_12$se,xmax=coarse_big_12$meanmass+coarse_big_12$se, width=0.5)+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="12 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_crs12
coarse_big_18<- coarse_big[coarse_big$incubation_time %in% c("18"), ]
bg_crs18<- ggplot(coarse_big_18, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= coarse_big_18$meanmass-coarse_big_18$se,xmax=coarse_big_18$meanmass+coarse_big_18$se, width=0.5)+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="18 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_crs18
coarse_big_24<- coarse_big[coarse_big$incubation_time %in% c("24"), ]
bg_crs24<- ggplot(coarse_big_24, aes(y = species, x= meanmass,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar(xmin= coarse_big_24$meanmass-coarse_big_24$se,xmax=coarse_big_24$meanmass+coarse_big_24$se, width=0.5)+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Percentage mass loss")+ theme(legend.position = "none")+
ggtitle(label="", subtitle ="24 months")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
#bg_crs24#library(patchwork)
#FigS2<- sm_crs6+sm_crs12+sm_crs18+sm_crs24+bg_crs6+bg_crs12+bg_crs18+bg_crs24+ plot_layout(ncol = 4)
#FigS2#ggsave(filename="FigS2.png", plot=FigS2, device="png",
# path=path, height=9, width=16, units="in", dpi=500)forth_harvest<- hav_datb[hav_datb$Harvest %in% c("4"), ]
forth_harvest$W_D_prop<- forth_harvest$f_bark_mass-0.01/forth_harvest$f_wood_mass-0.01
#write.csv(forth_harvest, "hv4.csv")
#plot(W_D_prop~ growth_form, data= forth_harvest)
#b_w_prop_fig<- ggplot(data = forth_harvest, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
# theme(legend.position= "none")+
# labs(x= "Growth form",y="Bark to wood mass proportion")+
# geom_boxplot(aes(fill = growth_form), color = "black")+facet_grid(rows = vars(diameter_class),cols = #vars(mesh_size),axes = "all", axis.labels = "all_x")
#b_w_prop_fig
#ggsave(filename="b_w_prop_fig.png", plot=b_w_prop_fig, device="png",
# path=path, height=4, width=5, units="in", dpi=500)
md1<- lmer(W_D_prop~(growth_form+mesh_size+diameter_class)^2 + (1|species), data = forth_harvest)
anova(md1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## growth_form 479.8 479.8 1 22.00 6.9695 0.014956 *
## mesh_size 12086.7 12086.7 1 349.10 175.5735 < 2.2e-16 ***
## diameter_class 2725.1 2725.1 1 350.23 39.5849 9.377e-10 ***
## growth_form:mesh_size 712.2 712.2 1 349.10 10.3459 0.001419 **
## growth_form:diameter_class 268.3 268.3 1 350.23 3.8975 0.049143 *
## mesh_size:diameter_class 1630.8 1630.8 1 349.34 23.6886 1.718e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1forth_harvest_small<- forth_harvest[forth_harvest$diameter_class == "2.5 cm",]
forth_harvest_big<- forth_harvest[forth_harvest$diameter_class == "5.0 cm",]
forth_harvest_small_fine<- forth_harvest_small[forth_harvest_small$mesh_size == "Invertebrates blocked",]
forth_harvest_small_crs<- forth_harvest_small[forth_harvest_small$mesh_size == "Invertebrates access",]
forth_harvest_big_fine<- forth_harvest_big[forth_harvest_big$mesh_size == "Invertebrates blocked",]
forth_harvest_big_crs<- forth_harvest_big[forth_harvest_big$mesh_size == "Invertebrates access",]
hist(forth_harvest_small_fine$W_D_prop)
md_sm_fn<- lmer(W_D_prop~ growth_form + (1|species), data = forth_harvest_small_fine)
anova(md_sm_fn)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## growth_form 152.19 152.19 1 22.256 4.9288 0.0369 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: W_D_prop ~ growth_form + (1 | species)
## Data: forth_harvest_small_fine
##
## REML criterion at convergence: 634.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1015 -0.4769 -0.1278 0.3997 3.4265
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 24.49 4.949
## Residual 30.88 5.557
## Number of obs: 97, groups: species, 24
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 11.851 1.633 21.956 7.259 2.88e-07 ***
## growth_formTrees -5.143 2.316 22.256 -2.220 0.0369 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## grwth_frmTr -0.705## R2m R2c
## [1,] 0.1075753 0.502334## [1] 97 74lblbwratio<-expression(paste( F["(1,23)"]," = 4.92"))
plot_sm_fn<- ggplot(data = forth_harvest_small_fine, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
theme(legend.position= "none")+
labs(x= "Growth form",y="Bark to wood mass proportion")+ggtitle("Inverterbrates blocked (2.5 cm)")+ geom_boxplot(aes(fill = growth_form), color = "black")+
annotate("text", x = 2, y = 30, size=3.5, hjust=0.5,label = as.character(lblbwratio), parse=TRUE) +
annotate("text", x = 2, y = 25, size=3.5, label = "p = 0.03")+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 9),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank())
plot_sm_fn
md_big_fn<- lmer(W_D_prop~ growth_form + (1|species), data = forth_harvest_big_fine)
anova(md_big_fn)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## growth_form 574.02 574.02 1 22.011 5.4955 0.0285 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] 94 74plot_big_fn<-ggplot(data = forth_harvest_big_fine, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
theme(legend.position= "none")+
labs(x= "Growth form",y="Bark to wood mass proportion")+ ggtitle("Invertebrates blocked (4.0 cm)")+ geom_boxplot(aes(fill = growth_form), color = "black")+
annotate("text", x = 2, y = 50, size=3.5, hjust=0.5,label = as.character(expression(paste( F["(1,22)"]," = 5.49")))) +
annotate("text", x = 2, y = 45, size=3.5, label = "p = 0.03")+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 9),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank())
plot_big_fn
md_big_crs<- lmer(W_D_prop~ growth_form + (1|species), data = forth_harvest_big_crs)
anova(md_big_crs)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## growth_form 49.147 49.147 1 21.594 1.9337 0.1785## [1] 94 74plot_big_crs<- ggplot(data = forth_harvest_big_crs, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
theme(legend.position= "none")+
labs(x= "Growth form",y="Bark to wood mass proportion")+
geom_boxplot(aes(fill = growth_form), color = "black")+ ggtitle("Invertebrates access (4.0 cm)")+
annotate("text", x = 1.6, y = 20, size=5.5, hjust=0.5,label = "")+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 9),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank())
hist(forth_harvest_small_crs$W_D_prop)
md_small_crs<- lmer(log(W_D_prop)~ growth_form + (1|species), data = forth_harvest_small_crs)
anova(md_small_crs)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## growth_form 13.104 13.104 1 14.925 5.2656 0.03668 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] 94 74plot_small_crs<- ggplot(data = forth_harvest_small_crs, aes(x = growth_form, y =W_D_prop)) + theme_bw()+
theme(legend.position= "none")+
labs(x= "Growth form",y="Bark to wood mass proportion")+
ggtitle("Invertebrates access (2.5 cm)")+
geom_boxplot(aes(fill = growth_form), color = "black")+
annotate("text", x = 2, y = 30, size=3.5, hjust=0.5,label = as.character()) +
annotate("text", x = 2, y = 25, size=3.5, label = "p = 0.03")+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 9),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank())
plot_small_crs
hv_4th_plt<- plot_big_crs+plot_big_fn+plot_small_crs+plot_sm_fn
#hv_4th_plt
### Plot the proportion of bark to wood dry mass ratio for lianas and trees after 24 months of field incubation
ggsave(filename="Figure 2.png", plot=hv_4th_plt, device="png",
path=path, height=6, width=7, units="in", dpi=500)10 Plot the mass loss of species
First we calculate the mean mass loss per species. To test hypothesis one we compare the diameter_class of lianas and trees WD in the absence of inverterbrates to do so we create a new data frame for the fine mesh size litterbags (Invertebrates blocked )
10.1 calculate invertebrates contribution to WD decomposition across different growthforms and WD diameter_class
ml_species<-hav_datb%>%
group_by(Time_months,mesh_size,diameter_class,growth_form,species)%>%
summarize(ml=mean(per_ml,na.rm = TRUE),
)
summary(ml_species)## Time_months mesh_size diameter_class growth_form
## : 0 Invertebrates access :192 2.5 cm:192 Lianas:192
## 12 months:96 Invertebrates blocked:192 5.0 cm:192 Trees :192
## 18 months:96
## 24 months:96
## 6 months :96
##
##
## species ml
## Ayenia grandifolia : 16 Min. :-15.07
## Bauhinia purpurea : 16 1st Qu.: 30.13
## Bridelia stipularis: 16 Median : 50.32
## Calamus henryanus : 16 Mean : 50.81
## Cayratia trifolia : 16 3rd Qu.: 71.25
## Celastrus sp1 : 16 Max. :100.00
## (Other) :288#spread stat column across multiple columns
ml_wide<- spread(ml_species, key=mesh_size, value=ml)
ml_wide$invt_ml<- ml_wide$`Invertebrates access` - ml_wide$`Invertebrates blocked`
summary(ml_wide)## Time_months diameter_class growth_form species
## : 0 2.5 cm:96 Lianas:96 Ayenia grandifolia : 8
## 12 months:48 5.0 cm:96 Trees :96 Bauhinia purpurea : 8
## 18 months:48 Bridelia stipularis: 8
## 24 months:48 Calamus henryanus : 8
## 6 months :48 Cayratia trifolia : 8
## Celastrus sp1 : 8
## (Other) :144
## Invertebrates access Invertebrates blocked invt_ml
## Min. : 3.035 Min. :-15.07 Min. :-12.29
## 1st Qu.: 49.902 1st Qu.: 19.89 1st Qu.: 18.44
## Median : 68.657 Median : 35.15 Median : 29.87
## Mean : 65.963 Mean : 35.66 Mean : 30.30
## 3rd Qu.: 82.054 3rd Qu.: 51.11 3rd Qu.: 40.46
## Max. :100.000 Max. : 88.24 Max. : 89.07
## inv_spec<-ml_wide%>%
group_by(Time_months,diameter_class, growth_form)%>%
summarize(mean_invt=mean(invt_ml,na.rm = TRUE),
sd=sd(invt_ml,na.rm = TRUE),
se=sd(invt_ml,na.rm = TRUE)/sqrt(n()),
)
inv_spec## # A tibble: 16 × 6
## # Groups: Time_months, diameter_class [8]
## Time_months diameter_class growth_form mean_invt sd se
## <fct> <fct> <fct> <dbl> <dbl> <dbl>
## 1 12 months 2.5 cm Lianas 35.2 23.3 6.73
## 2 12 months 2.5 cm Trees 29.4 14.8 4.26
## 3 12 months 5.0 cm Lianas 40.7 16.5 4.77
## 4 12 months 5.0 cm Trees 30.2 15.6 4.50
## 5 18 months 2.5 cm Lianas 25.2 17.7 5.10
## 6 18 months 2.5 cm Trees 29.1 10.7 3.09
## 7 18 months 5.0 cm Lianas 31.7 14.2 4.10
## 8 18 months 5.0 cm Trees 26.8 17.3 4.99
## 9 24 months 2.5 cm Lianas 35.9 16.0 4.61
## 10 24 months 2.5 cm Trees 29.3 16.4 4.72
## 11 24 months 5.0 cm Lianas 28.5 18.3 5.27
## 12 24 months 5.0 cm Trees 29.7 12.4 3.57
## 13 6 months 2.5 cm Lianas 26.8 28.2 8.15
## 14 6 months 2.5 cm Trees 26.6 20.4 5.89
## 15 6 months 5.0 cm Lianas 34.3 13.0 3.74
## 16 6 months 5.0 cm Trees 25.5 14.2 4.09ml_species<-hav_datb%>%
group_by(Time_months,mesh_size,diameter_class,growth_form)%>%
summarize(ml=mean(per_ml,na.rm = TRUE),
)
summary(ml_species)## Time_months mesh_size diameter_class growth_form
## :0 Invertebrates access :16 2.5 cm:16 Lianas:16
## 12 months:8 Invertebrates blocked:16 5.0 cm:16 Trees :16
## 18 months:8
## 24 months:8
## 6 months :8
##
## ml
## Min. :16.10
## 1st Qu.:37.00
## Median :53.11
## Mean :51.02
## 3rd Qu.:63.64
## Max. :89.6811 Decay rates for each species across mesh size and diameter class
hav_datb$incubation_time<- as.numeric(as.character(hav_datb$incubation_time))
hav_datb$time_yrs<- hav_datb$incubation_time/12
df2 <- hav_datb %>%
filter(hav_datb$per_ml >= 0)
#df2 <- hav_datb %>%
any(is.na(df2 $mass_remaining))## [1] FALSEdf2 $mass_remaining<- (df2$final_mass)/df2$initial_mass
df2 $incubation_time<- as.numeric(df2 $incubation_time)
dt<- df2 %>% dplyr::select(species, growth_form, block,diameter_class,mesh_size, time_yrs,mass_remaining)
data_aggregated <- dt %>%
group_by(time_yrs, species,growth_form,mesh_size,diameter_class,block) %>% # Group by the identifier and time columns
summarize(value = mean(mass_remaining, na.rm = TRUE)) %>% # Aggregate (take the mean)
ungroup()
any(is.na(data_aggregated $value))## [1] FALSE# Reshape the data for litterfitter
wood_decomp_wide <-data_aggregated %>%
pivot_wider(names_from = time_yrs, values_from = value,names_prefix = "time_",)
wood_decomp_wide$time_0<- 1
long_data2 <- wood_decomp_wide %>%
pivot_longer( cols = time_0.5:time_0,names_to = "time_yrs", values_to = "mass_remaining") %>%
mutate(time_yrs = gsub("time_", "", time_yrs)) #remove "_mass" from species name
any(is.na(long_data2$mass_remaining))## [1] TRUE## tibble [1,920 × 7] (S3: tbl_df/tbl/data.frame)
## $ species : Factor w/ 24 levels "Ayenia grandifolia",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ growth_form : Factor w/ 2 levels "Lianas","Trees": 1 1 1 1 1 1 1 1 1 1 ...
## $ mesh_size : Factor w/ 2 levels "Invertebrates access",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ diameter_class: Factor w/ 2 levels "2.5 cm","5.0 cm": 1 1 1 1 1 1 1 1 1 1 ...
## $ block : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 1 2 2 2 2 2 ...
## $ time_yrs : chr [1:1920] "0.5" "1" "1.5" "2" ...
## $ mass_remaining: num [1:1920] 0.405 0.464 0.638 0.357 1 ...long_data2$time_yrs<- as.numeric(as.character(long_data2$time_yrs))
library(litterfitter)
library(dplyr)
library(tibble)
library(litterfitter)
library(dplyr)
library(tibble)
lita<- na.omit(long_data2)
dim(lita)## [1] 1789 7## [1] 1920 7options(na.action = "na.omit")
# turn a list of dataframes into a long dataframe
list_to_df <- function(mylist){
# make a vector of row ids that correspond to the list names
rowid.indx <- lapply(mylist, function(x) dim(x)[1])
sourceVec.list <- list()
for(i in 1:length(rowid.indx)){
sourceName <- names(rowid.indx)[i]
numRows <- rowid.indx[[i]]
sourceVec.list[[i]] <- rep(sourceName, numRows)
}
rowVec <- unlist(sourceVec.list)
# combine into df
df <- data.frame(do.call(rbind, mylist))
df$source <- rowVec
return(df)
}
# calculate decay trajectory fits for each species+size
Calc_R2<-function(w.fits){
pred<-w.fits[['predicted']]
mass<-w.fits[['mass']]
sstot<-sum((mass - mean(mass))^2)
ssres<-sum((pred - mass)^2)
r2<-1-(ssres/sstot)
return(r2)
}
# decay fits
fit_all_curves<-function(df_in, lita){
#df_in <- pmr #testing stuff
#negative expon fit
ne_fits <- lapply(split(df_in, factor(df_in$species)),function(x){
fit_litter(time = x$time_yrs,
mass.remaining = x$mass_remaining, model = c("neg.exp"), iters = 1000)
})
k<-unlist(lapply(ne_fits, function(x) x$optimFit$par))
# put everything in a dataframe
spdf<-data.frame(k=k )
spdf$species<-rownames(spdf)
return(spdf)
}
neg.exp<-litterfitter:::neg.exp
decayfits<-lita %>%
group_by(species,growth_form,mesh_size,diameter_class,block) %>%
do(data.frame(fit_all_curves(.)))lt<-decayfits%>%
group_by(diameter_class, growth_form,mesh_size)%>%
summarize(mean_invt=mean(k,na.rm = TRUE),
sd=sd(k,na.rm = TRUE),
se=sd(k,na.rm = TRUE)/sqrt(n()),
)
lt## # A tibble: 8 × 6
## # Groups: diameter_class, growth_form [4]
## diameter_class growth_form mesh_size mean_invt sd se
## <fct> <fct> <fct> <dbl> <dbl> <dbl>
## 1 2.5 cm Lianas Invertebrates access 1.38 0.882 0.127
## 2 2.5 cm Lianas Invertebrates blocked 0.463 0.210 0.0303
## 3 2.5 cm Trees Invertebrates access 1.40 1.17 0.168
## 4 2.5 cm Trees Invertebrates blocked 0.444 0.218 0.0314
## 5 5.0 cm Lianas Invertebrates access 1.59 1.27 0.184
## 6 5.0 cm Lianas Invertebrates blocked 0.438 0.180 0.0261
## 7 5.0 cm Trees Invertebrates access 1.19 0.924 0.133
## 8 5.0 cm Trees Invertebrates blocked 0.414 0.192 0.0277## [1] 8 6lt_plot<- ggplot(lt, aes(x = diameter_class, y = as.numeric(as.character(mean_invt )),shape =mesh_size, colour=growth_form)) + ylab(NULL) +
geom_point(pch=22,size=0, color =("white"))+geom_rect(data=NULL,aes(xmin=1.5,xmax=2.5,ymin=-Inf,ymax=Inf),
fill="lightgray", colour = NA, alpha = 0.1) +
geom_point(position=position_dodge(0.3),stat="identity", size=2.5)+scale_y_continuous(limits = c(0,2))+
geom_errorbar(ymin = lt$mean_invt - lt$se, ymax = lt$mean_invt + lt$se, width=0.2, size = 0.5, position=position_dodge(0.3))+
xlab("Diameter class") +
ylab("Decomposition rate") + scale_x_discrete(labels= c("2.5 cm", "4.0 cm"))+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 15),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.position = c(0.4,0.9),
legend.direction = "vertical", legend.box = "horizontal",
legend.text=element_text(size=10,face="italic"),
legend.background = element_blank(),
legend.key.width=unit(0.1,"cm"),
legend.key.height=unit(0.1,"cm"))+
labs(color="Growth form",shape="Invertebrate status", size=10)+
theme(text=element_text(size=10, family="serif",face="bold"))+ theme(axis.title = element_text(family = "palatino", size = (12), colour = "black"))+
theme(axis.text = element_text(family = "palatino", colour = "black", size = (8)))Let save figure 4.
ggsave(filename="Figure 4.png", plot=lt_plot, device="png",
path=path, height=3, width=5, units="in", dpi=500)## [1] 8 6lt_fauna_plot<- ggplot(lt_fauna, aes(x = diameter_class, y = as.numeric(as.character(mean_invt )),colour=growth_form)) + ylab(NULL) +
geom_point(pch=22,size=0, color =("white"))+geom_rect(data=NULL,aes(xmin=1.5,xmax=2.5,ymin=-Inf,ymax=Inf),
fill="lightgray", colour = NA, alpha = 0.1) +
geom_point(position=position_dodge(0.3),stat="identity", size=3)+scale_y_continuous(limits = c(1,2))+
geom_errorbar(ymin = lt_fauna$mean_invt - lt_fauna$se, ymax = lt_fauna$mean_invt + lt_fauna$se, width=0.1, size = 0.8, position=position_dodge(0.3))+
xlab("Diameter class") +
ylab("Decomposition rate") + scale_x_discrete(labels= c("2.5 cm", "4.0 cm"))+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 15),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.position = c(0.2,0.9),
legend.direction = "vertical", legend.box = "horizontal",
legend.text=element_text(size=10,face="italic"),
legend.background = element_blank(),
legend.key.width=unit(0.1,"cm"),
legend.key.height=unit(0.1,"cm"))+
labs(color="Growth form",shape="Invertebrate status", size=10)+
theme(text=element_text(size=10, family="serif",face="bold"))+ theme(axis.title = element_text(family = "palatino", size = (12), colour = "black"))+
theme(axis.text = element_text(family = "palatino", colour = "black", size = (8)))
lt_fauna_plot
ggsave(filename="Figure 1.png", plot=lt_fauna_plot, device="png",
path=path, height=3, width=5, units="in", dpi=500)11.1 Merge k values with the initial traits
dt_f<- decayfits %>% left_join(traits_wide, by= c("species"))
f_d<- dt_f%>% left_join(data_density, by= c("growth_form", "species"))
dt_fb<- f_d %>% left_join(spc_dens_wdbark, by= c("species","diameter_class"))mean_k<- dt_fb%>% group_by(growth_form,species,diameter_class,mesh_size)%>%
summarise(k_mean= mean(k),
se= sd(k)/sqrt(n()))
big<- mean_k[mean_k$diameter_class %in% c("5.0 cm"), ]
big_crs<- big[big$mesh_size %in% c("Invertebrates access"), ]
big_fine<- big[big$mesh_size %in% c("Invertebrates blocked"), ]
small<- mean_k[mean_k$diameter_class %in% c("2.5 cm"), ]
small_crs<- small[small$mesh_size %in% c("Invertebrates access"), ]
small_fine<- small[small$mesh_size %in% c("Invertebrates blocked"), ]
bg_crs_plot<- ggplot(big_crs, aes(y = species, x= k_mean,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar( data=big_crs, aes(xmin=k_mean-se,xmax=k_mean+se, width=0.5))+
scale_x_continuous(limits = c(0,5.0))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank(),
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Decomposition rate (k)")+
theme(legend.position = "none")+
ggtitle(label="
4.0 cm wood - inverterbrates present")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
bg_crs_plot
bg_fine_plot<- ggplot(big_fine, aes(y = species, x= k_mean,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar( data=big_fine, aes(xmin=k_mean-se,xmax=k_mean+se, width=0.5))+
scale_x_continuous(limits = c(0,5.0))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_blank() ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Decomposition rate (k)")+
theme(legend.position = "none")+
ggtitle(label="
4.0 cm wood - inverterbrates absent")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
bg_fine_plot
sm_crs_plot<- ggplot(small_crs, aes(y = species, x= k_mean,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar( data=small_crs, aes(xmin=k_mean-se,xmax=k_mean+se, width=0.5))+
scale_x_continuous(limits = c(0,5.0))+
guides(fill = guide_colourbar(title = "Growth form"))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_text(size = 9, face = "italic") ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+ labs(x= "Decomposition rate (k)")+
theme(legend.position = "none")+
ggtitle(label="
2.5 cm wood - inverterbrates present")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
sm_crs_plot
sm_fine_plot<- ggplot(small_fine, aes(y = species, x= k_mean,fill= growth_form)) +
geom_bar(stat = "identity", position = "dodge")+
geom_errorbar( data=small_fine, aes(xmin=k_mean-se,xmax=k_mean+se, width=0.5))+
scale_x_continuous(limits = c(0,5.0))+
theme(axis.title.y =element_blank(),
axis.ticks.y=element_blank(),
axis.text.y =element_text(size = 9, face = "italic") ,
axis.text.x =element_text(size=10,colour="black"),
axis.title.x =element_text(size=12, colour = "black"))+
labs(x= "Decomposition rate (k)", fill= "Growth form")+
theme(legend.position = c(0.8,0.8))+
ggtitle(label="
2.5 cm wood - inverterbrates absent")+ theme(
plot.title = element_text(color = "black", size = 12, face = "bold"),
plot.subtitle = element_text(color = "black",size = 12))+
theme( plot.subtitle = element_text(hjust = 0.5))+ theme(plot.margin = margin(0, 0, 0, 0, "pt"))
sm_fine_plot
## Plot liana and tree wood mean decomposition rate per year
ggsave(filename="Figure S5.png", plot=k_plot, device="png",
path=path, height=8, width=8, units="in", dpi=500)## [1] 384 36liana_tree2 <- dt_fb[!apply(dt_fb[,c( 'k',"mesh_size", "diameter_class","block", "mean_density","avg_brkwd_ratio",
"Nitrogen_wood", "Carbon_wood", "cellulose_wood", "hemicellulose_wood",
"Tannins_wood","Calcium_wood","Potassium_wood", "magesium_wood","Manganese_wood",
"Phosphorous_wood","sugars_wood", "Carbon_bark", "Nitrogen_bark",
"cellulose_bark", "hemicellulose_bark","ADL_bark","Tannins_bark","Calcium_bark",
"Potassium_bark", "magesium_bark","Manganese_bark","Phosphorous_bark",
"sugars_bark","Silicon_wood","Silicon_bark")], 1, anyNA),]
dim(liana_tree2) ## [1] 336 36liana_tree2$C_N<- liana_tree2$Carbon_wood/liana_tree2$Nitrogen_wood
liana_tree2$lig_N<- liana_tree2$ADL_wood/liana_tree2$Nitrogen_wood
dim(liana_tree2)## [1] 336 38pcn<- ungroup(liana_tree2) %>% dplyr::select(Carbon_wood, Nitrogen_wood, cellulose_wood, hemicellulose_wood, ADL_wood,Tannins_wood,Calcium_wood, Potassium_wood,magesium_wood,Manganese_wood,Phosphorous_wood,sugars_wood,Silicon_wood, C_N,lig_N)
dim(pcn)## [1] 336 15res<- cor(pcn, method = "pearson")
corrplot::corrplot(res, method = "color",order = "hclust", tl.pos = 'n')
library(ggcorrplot)
# Get the upeper triangle
ggcorrplot(res, hc.order = TRUE, type = "upper",
outline.col = "white")
wdpca<- prcomp(pcn, scale=TRUE)
# getting PCA scores
s <- as.data.frame(wdpca$x)
s <- dplyr::select(s, PC1:PC6)
s$mesh_size <- liana_tree2$mesh_size
s$growth_form <- liana_tree2$growth_form
s$density <- liana_tree2$avg_density
s$bark_wd_ratio<- liana_tree2$avg_brkwd_ratio
s$species<- liana_tree2$species
s$k<- liana_tree2$k
s$block<- liana_tree2$block12 Bark pca
liana_tree2$C_N_bark<- liana_tree2$Carbon_bark/liana_tree2$Nitrogen_bark
liana_tree2$lig_N_bark<- liana_tree2$ADL_bark/liana_tree2$Nitrogen_bark
dim(liana_tree2)## [1] 336 40pcnb<- ungroup(liana_tree2) %>% dplyr::select(Carbon_bark, Nitrogen_bark, cellulose_bark, hemicellulose_bark, ADL_bark,Tannins_bark, Calcium_bark,Potassium_bark,magesium_bark,Manganese_wood,Phosphorous_bark,sugars_wood,Silicon_bark,C_N_bark,lig_N_bark)
dim(pcnb)## [1] 336 15resb<- cor(pcnb, method = "pearson")
corrplot::corrplot(resb, method = "color",order = "hclust", tl.pos = 'n')
# getting PCA scores for the bark
bakpca <- prcomp(pcnb, scale=TRUE)
s_bark <- as.data.frame(bakpca$x)
s_bark <- dplyr::select(s_bark, PC1:PC6)
s$PC1_bark<- s_bark$PC1
s$PC2_bark<- s_bark$PC2
s$PC3_bark<- s_bark$PC3
s$diameter_class<- liana_tree2$diameter_classlibrary(FactoMineR)
library(ggplot2)
library(ggrepel)
library(reshape2)
#env=rapply(env,scale,c("numeric"),how="replace")
pca_wd <- PCA(liana_tree2[ ,c("growth_form","C_N","lig_N","ADL_wood", "Tannins_wood", "hemicellulose_wood", "Nitrogen_wood","Carbon_wood","cellulose_wood","Calcium_wood","Potassium_wood","magesium_wood", "Manganese_wood", "Phosphorous_wood", "sugars_wood","Silicon_bark")],scale.unit = TRUE, quali.sup = c(1:1), graph = FALSE)
summary(pca_wd)##
## Call:
## PCA(X = liana_tree2[, c("growth_form", "C_N", "lig_N", "ADL_wood",
## "Tannins_wood", "hemicellulose_wood", "Nitrogen_wood", "Carbon_wood",
## "cellulose_wood", "Calcium_wood", "Potassium_wood", "magesium_wood",
## "Manganese_wood", "Phosphorous_wood", "sugars_wood", "Silicon_bark")],
## scale.unit = TRUE, quali.sup = c(1:1), graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 5.454 2.215 1.857 1.652 1.079 0.771 0.648
## % of var. 36.362 14.770 12.381 11.011 7.193 5.137 4.323
## Cumulative % of var. 36.362 51.132 63.513 74.524 81.717 86.854 91.177
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 0.572 0.347 0.192 0.089 0.050 0.049 0.015
## % of var. 3.812 2.315 1.278 0.591 0.336 0.327 0.100
## Cumulative % of var. 94.989 97.304 98.582 99.174 99.510 99.837 99.937
## Dim.15
## Variance 0.009
## % of var. 0.063
## Cumulative % of var. 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## 2 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## 3 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## 4 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## 5 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## 6 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## 7 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## 8 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## 9 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## 10 | 2.051 | -0.931 0.047 0.206 | -0.574 0.044 0.078 |
## Dim.3 ctr cos2
## 1 0.248 0.010 0.015 |
## 2 0.248 0.010 0.015 |
## 3 0.248 0.010 0.015 |
## 4 0.248 0.010 0.015 |
## 5 0.248 0.010 0.015 |
## 6 0.248 0.010 0.015 |
## 7 0.248 0.010 0.015 |
## 8 0.248 0.010 0.015 |
## 9 0.248 0.010 0.015 |
## 10 0.248 0.010 0.015 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
## C_N | 0.873 13.983 0.763 | -0.062 0.174 0.004 | 0.141
## lig_N | 0.857 13.473 0.735 | 0.333 4.993 0.111 | 0.285
## ADL_wood | 0.402 2.957 0.161 | 0.756 25.807 0.572 | 0.393
## Tannins_wood | -0.069 0.086 0.005 | -0.157 1.108 0.025 | 0.185
## hemicellulose_wood | -0.378 2.624 0.143 | 0.027 0.034 0.001 | -0.724
## Nitrogen_wood | -0.750 10.301 0.562 | 0.163 1.198 0.027 | -0.101
## Carbon_wood | 0.800 11.748 0.641 | -0.016 0.011 0.000 | 0.058
## cellulose_wood | 0.361 2.389 0.130 | -0.698 21.965 0.487 | -0.040
## Calcium_wood | -0.629 7.245 0.395 | 0.508 11.626 0.258 | -0.031
## Potassium_wood | -0.678 8.426 0.460 | 0.023 0.024 0.001 | 0.506
## ctr cos2
## C_N 1.066 0.020 |
## lig_N 4.386 0.081 |
## ADL_wood 8.320 0.155 |
## Tannins_wood 1.850 0.034 |
## hemicellulose_wood 28.195 0.524 |
## Nitrogen_wood 0.547 0.010 |
## Carbon_wood 0.181 0.003 |
## cellulose_wood 0.088 0.002 |
## Calcium_wood 0.050 0.001 |
## Potassium_wood 13.764 0.256 |
##
## Supplementary categories
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## Lianas | 1.459 | -1.120 0.590 -9.209 | 0.763 0.274 9.841 |
## Trees | 1.605 | 1.232 0.590 9.209 | -0.839 0.274 -9.841 |
## Dim.3 cos2 v.test
## Lianas -0.380 0.068 -5.354 |
## Trees 0.418 0.068 5.354 |#plot.PCA(pca_wd)
# extract pc scores for first two component and add to dat dataframe
liana_tree2$pc1_wood <- pca_wd$ind$coord[, 1] # indexing the first column
liana_tree2$pc2_wood <- pca_wd$ind$coord[, 2] # indexing the second column
pca_vars_wd <- pca_wd$var$coord %>% data.frame
pca_vars_wd $vars <- rownames(pca_vars_wd )
pca_vars_wd_m <- melt(pca_vars_wd, id.vars = "vars")
library(ggplot2)
wd_pcaplot <- ggplot(data = liana_tree2, aes(x = pc1_wood*-1, y = pc2_wood*-1)) +
geom_hline(yintercept = 0, lty = 2) +
geom_vline(xintercept = 0, lty = 2) +
geom_point(alpha = 0.5,size= 3, aes(colour=growth_form)) +scale_shape_manual(values=c(15,19,17))+
scale_colour_manual(values = c("lightcoral", "#00E5EE"))+
geom_segment(data = pca_vars_wd, aes(x = 0, xend = Dim.1*5.5*-1, y = 0, yend = Dim.2*5.5*-1),arrow = arrow(length = unit(0.025, "npc"), type = "open"), lwd = 0.7, colour= "gray") +
xlab("PC1 36.66%") + ylab("PC2 14.77%") + coord_equal() + theme_minimal() +
geom_text_repel(data = pca_vars_wd ,size=2.7, fontface = 'bold', min.segment.length = Inf,max.overlaps = Inf,
hjust = 0, segment.size = 0.2,aes(x = Dim.1*6.2*-1, y = Dim.2*6.2*-1,
label = c("C:N", "Lignin:N","Lignin", "Condensed tannins","Hemicellulose", "N","C","Cellulose","Ca","K","Mg","Mn","P","Sugars","Si")),)+
theme(legend.position = c(0.8,0.8))+
theme(text = element_text(size = 10,face="bold"), panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5))+
theme(plot.title = element_text(size = 10))+ labs(colour= "Growth form",title = "Wood traits")
wd_pcaplot
#bark PCA plots
pca_brk<- PCA(liana_tree2[ ,c("growth_form","C_N","lig_N","ADL_bark", "Tannins_bark", "hemicellulose_bark", "Nitrogen_bark","Carbon_bark","cellulose_bark","Calcium_bark","Potassium_bark","magesium_bark", "Manganese_bark", "Phosphorous_bark", "sugars_bark","Silicon_bark")],scale.unit = TRUE, quali.sup = c(1:1), graph = FALSE)
summary(pca_brk)##
## Call:
## PCA(X = liana_tree2[, c("growth_form", "C_N", "lig_N", "ADL_bark",
## "Tannins_bark", "hemicellulose_bark", "Nitrogen_bark", "Carbon_bark",
## "cellulose_bark", "Calcium_bark", "Potassium_bark", "magesium_bark",
## "Manganese_bark", "Phosphorous_bark", "sugars_bark", "Silicon_bark")],
## scale.unit = TRUE, quali.sup = c(1:1), graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 3.938 3.244 1.832 1.509 1.139 1.083 0.698
## % of var. 26.253 21.625 12.214 10.063 7.595 7.220 4.653
## Cumulative % of var. 26.253 47.878 60.092 70.155 77.750 84.969 89.623
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 0.516 0.350 0.287 0.168 0.126 0.062 0.039
## % of var. 3.437 2.335 1.915 1.117 0.840 0.416 0.260
## Cumulative % of var. 93.060 95.395 97.311 98.428 99.267 99.683 99.943
## Dim.15
## Variance 0.009
## % of var. 0.057
## Cumulative % of var. 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
## 1 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 2 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 3 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 4 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 5 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 6 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 7 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 8 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 9 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## 10 | 5.218 | 3.516 0.935 0.454 | 2.553 0.598 0.239 | 0.216
## ctr cos2
## 1 0.008 0.002 |
## 2 0.008 0.002 |
## 3 0.008 0.002 |
## 4 0.008 0.002 |
## 5 0.008 0.002 |
## 6 0.008 0.002 |
## 7 0.008 0.002 |
## 8 0.008 0.002 |
## 9 0.008 0.002 |
## 10 0.008 0.002 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3
## C_N | -0.716 13.024 0.513 | 0.161 0.803 0.026 | 0.236
## lig_N | -0.728 13.447 0.530 | -0.056 0.098 0.003 | 0.235
## ADL_bark | -0.103 0.268 0.011 | -0.855 22.510 0.730 | -0.043
## Tannins_bark | -0.307 2.388 0.094 | 0.808 20.118 0.653 | -0.085
## hemicellulose_bark | 0.064 0.105 0.004 | -0.240 1.771 0.057 | 0.075
## Nitrogen_bark | 0.676 11.620 0.458 | -0.200 1.234 0.040 | 0.399
## Carbon_bark | -0.693 12.184 0.480 | -0.195 1.177 0.038 | 0.216
## cellulose_bark | -0.249 1.576 0.062 | 0.582 10.454 0.339 | 0.020
## Calcium_bark | 0.368 3.430 0.135 | 0.089 0.242 0.008 | -0.794
## Potassium_bark | 0.804 16.400 0.646 | 0.079 0.191 0.006 | 0.325
## ctr cos2
## C_N 3.033 0.056 |
## lig_N 3.016 0.055 |
## ADL_bark 0.102 0.002 |
## Tannins_bark 0.398 0.007 |
## hemicellulose_bark 0.311 0.006 |
## Nitrogen_bark 8.677 0.159 |
## Carbon_bark 2.547 0.047 |
## cellulose_bark 0.022 0.000 |
## Calcium_bark 34.441 0.631 |
## Potassium_bark 5.770 0.106 |
##
## Supplementary categories
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## Lianas | 1.416 | 0.838 0.350 8.105 | -0.918 0.421 -9.787 |
## Trees | 1.558 | -0.922 0.350 -8.105 | 1.010 0.421 9.787 |
## Dim.3 cos2 v.test
## Lianas -0.517 0.133 -7.337 |
## Trees 0.569 0.133 7.337 |#plot.PCA(pca_brk)
library(reshape2)
# extract pc scores for first two component and add to dat dataframe
liana_tree2$pc1_bark <- pca_brk$ind$coord[, 1] # indexing the first column
liana_tree2$pc2_bark <- pca_brk$ind$coord[, 2] # indexing the second column
pca_vars_brk <- pca_brk$var$coord %>% data.frame
pca_vars_brk$vars <- rownames(pca_vars_brk)
pca_vars_brk.m <- melt(pca_vars_brk, id.vars = "vars")
library(ggplot2)
brk_pcaplot <- ggplot(data = liana_tree2, aes(x = pc1_bark, y = pc2_bark)) +
geom_hline(yintercept = 0, lty = 2) +
geom_vline(xintercept = 0, lty = 2) +
geom_point(alpha = 0.5,size= 3, aes(colour=growth_form)) +scale_shape_manual(values=c(15,19,17))+
scale_colour_manual(values = c("lightcoral", "#00E5EE"))+
geom_segment(data = pca_vars_brk, aes(x = 0, xend = Dim.1*5.5, y = 0, yend = Dim.2*5.5),arrow = arrow(length = unit(0.025, "npc"), type = "open"), lwd = 0.7,colour= "gray") +
xlab("PC1 26.25.0%") + ylab("PC2 21.63%") + coord_equal() + theme_minimal() +
geom_text_repel(data = pca_vars_brk,size=2.7, fontface = 'bold', min.segment.length = Inf,max.overlaps = Inf,
hjust = 0, segment.size = 0.2,aes(x = Dim.1*6, y = Dim.2*6,
label = c("C:N", "Lignin:N","Lignin", "Condensed tannins","Hemicellulose", "N","C","Cellulose","Ca","K","Mg","Mn","P","Sugars","Si")),)+
theme(legend.position = "none")+
theme(text = element_text(size = 10,face="bold"), panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
panel.border = element_rect(colour = "black", fill=NA, size=1.5))+ labs(title = "Bark traits")+
theme(plot.title = element_text(size = 10))
brk_pcaplot
ggsave(filename="Figure 3.png", plot=pcaplot, device="png",
path=path, height=5, width=8, units="in", dpi=500)
mod2<- lmer(k_trans~ mesh_size+mesh_size:PC1+mesh_size:PC2+ mesh_size:PC1_bark+
mesh_size:PC2_bark+mesh_size:growth_form +mesh_size:diameter_class+
diameter_class+diameter_class:PC2+diameter_class:PC1+growth_form+PC1_bark+
diameter_class:growth_form+density:mesh_size+bark_wd_ratio:mesh_size+
PC2_bark+PC1+PC2+density+bark_wd_ratio+ (1|block)+ (1|species),data=s)
anova(mod2)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.0839 6.0839 1 298.448 31.2531 5.113e-08 ***
## diameter_class 0.0007 0.0007 1 273.182 0.0037 0.951405
## growth_form 0.0060 0.0060 1 15.641 0.0310 0.862413
## PC1_bark 0.0365 0.0365 1 16.904 0.1873 0.670606
## PC2_bark 0.1155 0.1155 1 13.890 0.5932 0.454102
## PC1 0.2465 0.2465 1 13.958 1.2661 0.279482
## PC2 0.1799 0.1799 1 16.090 0.9241 0.350630
## density 0.3948 0.3948 1 40.033 2.0279 0.162185
## bark_wd_ratio 0.0244 0.0244 1 28.413 0.1256 0.725701
## mesh_size:PC1 0.9246 0.9246 1 298.448 4.7498 0.030083 *
## mesh_size:PC2 0.1633 0.1633 1 298.448 0.8388 0.360485
## mesh_size:PC1_bark 1.0315 1.0315 1 298.448 5.2989 0.022027 *
## mesh_size:PC2_bark 0.8918 0.8918 1 298.448 4.5812 0.033134 *
## mesh_size:growth_form 0.4577 0.4577 1 298.448 2.3514 0.126228
## mesh_size:diameter_class 0.1241 0.1241 1 298.448 0.6375 0.425262
## PC2:diameter_class 0.0029 0.0029 1 306.284 0.0149 0.902805
## PC1:diameter_class 0.0020 0.0020 1 310.281 0.0103 0.919118
## growth_form:diameter_class 0.0963 0.0963 1 305.326 0.4949 0.482264
## mesh_size:density 1.4034 1.4034 1 298.448 7.2096 0.007657 **
## mesh_size:bark_wd_ratio 0.0525 0.0525 1 298.448 0.2698 0.603818
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.202862229 0.413429945
## sd_(Intercept)|block 0.069381370 0.368359715
## sigma 0.399729139 0.467743020
## (Intercept) 3.429460701 5.345746438
## mesh_sizeInvertebrates blocked -2.665048911 -1.242682744
## diameter_class5.0 cm -0.106670721 0.269521750
## growth_formTrees -0.290400060 0.726821624
## PC1_bark -0.082120594 0.112320248
## PC2_bark -0.110840743 0.094978670
## PC1 -0.004112065 0.170923195
## PC2 -0.039206530 0.228779581
## density -4.082266743 -0.527757744
## bark_wd_ratio -0.286270523 0.316618682
## mesh_sizeInvertebrates blocked:PC1 -0.116698968 -0.007299318
## mesh_sizeInvertebrates blocked:PC2 -0.131021105 0.046477680
## mesh_sizeInvertebrates blocked:PC1_bark -0.144528194 -0.012969241
## mesh_sizeInvertebrates blocked:PC2_bark -0.140524636 -0.007524164
## mesh_sizeInvertebrates blocked:growth_formTrees -0.580724400 0.065425615
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.266801195 0.110240844
## PC2:diameter_class5.0 cm -0.082045580 0.069613297
## PC1:diameter_class5.0 cm -0.046480954 0.053467645
## growth_formTrees:diameter_class5.0 cm -0.349583399 0.164586952
## mesh_sizeInvertebrates blocked:density 0.539789094 3.263129227
## mesh_sizeInvertebrates blocked:bark_wd_ratio -0.157396111 0.273920525## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.0839 6.0839 1 299.397 31.3527 4.87e-08 ***
## diameter_class 0.0006 0.0006 1 280.267 0.0030 0.956515
## growth_form 0.0070 0.0070 1 15.858 0.0358 0.852269
## PC1_bark 0.0349 0.0349 1 17.160 0.1800 0.676610
## PC2_bark 0.1153 0.1153 1 13.958 0.5941 0.453694
## PC1 0.2456 0.2456 1 14.029 1.2659 0.279421
## PC2 0.1848 0.1848 1 16.246 0.9523 0.343448
## density 0.4080 0.4080 1 41.258 2.1026 0.154611
## bark_wd_ratio 0.0312 0.0312 1 33.554 0.1608 0.690942
## mesh_size:PC1 0.9246 0.9246 1 299.397 4.7650 0.029821 *
## mesh_size:PC2 0.1633 0.1633 1 299.397 0.8415 0.359720
## mesh_size:PC1_bark 1.0315 1.0315 1 299.397 5.3158 0.021817 *
## mesh_size:PC2_bark 0.8918 0.8918 1 299.397 4.5958 0.032855 *
## mesh_size:growth_form 0.4577 0.4577 1 299.397 2.3589 0.125625
## mesh_size:diameter_class 0.1241 0.1241 1 299.397 0.6395 0.424524
## diameter_class:PC2 0.0016 0.0016 1 312.600 0.0083 0.927668
## diameter_class:growth_form 0.1703 0.1703 1 300.439 0.8777 0.349581
## mesh_size:density 1.4034 1.4034 1 299.397 7.2325 0.007561 **
## mesh_size:bark_wd_ratio 0.0525 0.0525 1 299.397 0.2707 0.603242
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.2029712783 0.413734479
## sd_(Intercept)|block 0.0693783335 0.368387981
## sigma 0.3997250460 0.467740724
## (Intercept) 3.4270046559 5.340812978
## mesh_sizeInvertebrates blocked -2.6650435517 -1.242688103
## diameter_class5.0 cm -0.0838469914 0.257702211
## growth_formTrees -0.2675293399 0.720104590
## PC1_bark -0.0808984465 0.112478464
## PC2_bark -0.1108646954 0.095066038
## PC1 0.0003113026 0.169449210
## PC2 -0.0394148642 0.228650020
## density -4.0852613417 -0.550131466
## bark_wd_ratio -0.2726347664 0.311869049
## mesh_sizeInvertebrates blocked:PC1 -0.1166985554 -0.007299730
## mesh_sizeInvertebrates blocked:PC2 -0.1310204361 0.046477011
## mesh_sizeInvertebrates blocked:PC1_bark -0.1445276981 -0.012969737
## mesh_sizeInvertebrates blocked:PC2_bark -0.1405241347 -0.007524666
## mesh_sizeInvertebrates blocked:growth_formTrees -0.5807219649 0.065423180
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.2667997744 0.110239423
## diameter_class5.0 cm:PC2 -0.0750266579 0.066391320
## diameter_class5.0 cm:growth_formTrees -0.3128842884 0.107038207
## mesh_sizeInvertebrates blocked:density 0.5397993551 3.263118965
## mesh_sizeInvertebrates blocked:bark_wd_ratio -0.1573944855 0.273918900## [1] 559.8641## [1] 552.4305## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.0839 6.0839 1 300.360 31.4542 4.633e-08 ***
## diameter_class 0.0005 0.0005 1 285.515 0.0024 0.960926
## growth_form 0.0076 0.0076 1 15.966 0.0395 0.844894
## PC1_bark 0.0337 0.0337 1 17.293 0.1744 0.681369
## PC2_bark 0.1150 0.1150 1 13.996 0.5945 0.453515
## PC1 0.2447 0.2447 1 14.068 1.2651 0.279514
## PC2 0.1883 0.1883 1 16.326 0.9733 0.338259
## density 0.4188 0.4188 1 42.187 2.1654 0.148570
## bark_wd_ratio 0.0366 0.0366 1 37.230 0.1892 0.666097
## mesh_size:PC1 0.9246 0.9246 1 300.360 4.7804 0.029557 *
## mesh_size:PC2 0.1633 0.1633 1 300.360 0.8442 0.358942
## mesh_size:PC1_bark 1.0315 1.0315 1 300.360 5.3330 0.021604 *
## mesh_size:PC2_bark 0.8918 0.8918 1 300.360 4.6107 0.032573 *
## mesh_size:growth_form 0.4577 0.4577 1 300.360 2.3665 0.125014
## mesh_size:diameter_class 0.1241 0.1241 1 300.360 0.6416 0.423774
## diameter_class:growth_form 0.2308 0.2308 1 309.056 1.1931 0.275549
## mesh_size:density 1.4034 1.4034 1 300.360 7.2559 0.007464 **
## mesh_size:bark_wd_ratio 0.0525 0.0525 1 300.360 0.2716 0.602655
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.2030508479 0.413949234
## sd_(Intercept)|block 0.0693755121 0.368406321
## sigma 0.3997226607 0.467739611
## (Intercept) 3.4255801317 5.339200362
## mesh_sizeInvertebrates blocked -2.6650405995 -1.242691056
## diameter_class5.0 cm -0.0726070661 0.252797995
## growth_formTrees -0.2550284257 0.717599839
## PC1_bark -0.0800198873 0.112612670
## PC2_bark -0.1108806436 0.095129789
## PC1 0.0002164365 0.169402962
## PC2 -0.0385287035 0.224938951
## density -4.0871738580 -0.568612581
## bark_wd_ratio -0.2637895923 0.309213291
## mesh_sizeInvertebrates blocked:PC1 -0.1166983284 -0.007299957
## mesh_sizeInvertebrates blocked:PC2 -0.1310200676 0.046476643
## mesh_sizeInvertebrates blocked:PC1_bark -0.1445274250 -0.012970010
## mesh_sizeInvertebrates blocked:PC2_bark -0.1405238586 -0.007524942
## mesh_sizeInvertebrates blocked:growth_formTrees -0.5807206238 0.065421839
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.2667989915 0.110238641
## diameter_class5.0 cm:growth_formTrees -0.2962964313 0.078927151
## mesh_sizeInvertebrates blocked:density 0.5398050086 3.263113312
## mesh_sizeInvertebrates blocked:bark_wd_ratio -0.1573935903 0.273918004## [1] 545.6784## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.2167 6.2167 1 301.360 32.1577 3.33e-08 ***
## diameter_class 0.0005 0.0005 1 286.235 0.0024 0.96093
## growth_form 0.0076 0.0076 1 15.967 0.0396 0.84485
## PC1_bark 0.0337 0.0337 1 17.293 0.1744 0.68138
## PC2_bark 0.1149 0.1149 1 13.996 0.5945 0.45351
## PC1 0.2446 0.2446 1 14.068 1.2651 0.27952
## PC2 0.1882 0.1882 1 16.326 0.9734 0.33824
## density 0.4187 0.4187 1 42.208 2.1658 0.14853
## bark_wd_ratio 0.0366 0.0366 1 37.247 0.1894 0.66593
## mesh_size:PC1 1.0723 1.0723 1 301.360 5.5466 0.01916 *
## mesh_size:PC1_bark 0.8809 0.8809 1 301.360 4.5566 0.03360 *
## mesh_size:PC2_bark 0.8036 0.8036 1 301.360 4.1570 0.04234 *
## mesh_size:growth_form 0.5087 0.5087 1 301.360 2.6316 0.10580
## mesh_size:diameter_class 0.0995 0.0995 1 301.360 0.5148 0.47361
## diameter_class:growth_form 0.2308 0.2308 1 310.078 1.1937 0.27544
## mesh_size:density 1.2701 1.2701 1 301.360 6.5701 0.01086 *
## mesh_size:bark_wd_ratio 0.1198 0.1198 1 301.360 0.6199 0.43171
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] 545.6784## [1] 540.2025## 2.5 % 97.5 %
## sd_(Intercept)|species 0.202964935 0.413895602
## sd_(Intercept)|block 0.069325928 0.368388276
## sigma 0.400284820 0.468397354
## (Intercept) 3.369401100 5.264980641
## mesh_sizeInvertebrates blocked -2.480593911 -1.166164267
## diameter_class5.0 cm -0.076826995 0.248532421
## growth_formTrees -0.248521660 0.723979563
## PC1_bark -0.086486998 0.103606879
## PC2_bark -0.113020684 0.092817177
## PC1 0.002294043 0.171309425
## PC2 -0.052530055 0.196742754
## density -3.907298382 -0.443535542
## bark_wd_ratio -0.276062345 0.294597199
## mesh_sizeInvertebrates blocked:PC1 -0.120113839 -0.011848851
## mesh_sizeInvertebrates blocked:PC1_bark -0.120532620 -0.005999656
## mesh_sizeInvertebrates blocked:PC2_bark -0.135503921 -0.003640302
## mesh_sizeInvertebrates blocked:growth_formTrees -0.593021377 0.051715347
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.257728978 0.118147607
## diameter_class5.0 cm:growth_formTrees -0.296571118 0.079171134
## mesh_sizeInvertebrates blocked:density 0.393060789 2.800106136
## mesh_sizeInvertebrates blocked:bark_wd_ratio -0.123557618 0.293502517## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.2795 6.2795 1 302.359 32.5236 2.802e-08 ***
## diameter_class 0.0005 0.0005 1 286.957 0.0024 0.960948
## growth_form 0.0076 0.0076 1 15.968 0.0396 0.844759
## PC1_bark 0.0337 0.0337 1 17.295 0.1743 0.681422
## PC2_bark 0.1148 0.1148 1 13.997 0.5945 0.453512
## PC1 0.2443 0.2443 1 14.069 1.2651 0.279514
## PC2 0.1880 0.1880 1 16.327 0.9735 0.338196
## density 0.4183 0.4183 1 42.255 2.1667 0.148435
## bark_wd_ratio 0.0367 0.0367 1 37.284 0.1899 0.665512
## mesh_size:PC1 1.0591 1.0591 1 302.359 5.4854 0.019825 *
## mesh_size:PC1_bark 1.1684 1.1684 1 302.359 6.0517 0.014453 *
## mesh_size:PC2_bark 0.7899 0.7899 1 302.359 4.0912 0.043988 *
## mesh_size:growth_form 0.9129 0.9129 1 302.359 4.7282 0.030446 *
## mesh_size:diameter_class 0.1177 0.1177 1 302.359 0.6094 0.435615
## diameter_class:growth_form 0.2307 0.2307 1 311.100 1.1950 0.275174
## mesh_size:density 1.3462 1.3462 1 302.359 6.9721 0.008709 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.202901121 0.413857224
## sd_(Intercept)|block 0.069289408 0.368372100
## sigma 0.400696908 0.468879510
## (Intercept) 3.321716898 5.197674839
## mesh_sizeInvertebrates blocked -2.302793505 -1.114213002
## diameter_class5.0 cm -0.073892910 0.251471675
## growth_formTrees -0.215645837 0.747712621
## PC1_bark -0.082875804 0.106600814
## PC2_bark -0.113334062 0.092515617
## PC1 0.002080089 0.171106891
## PC2 -0.052542165 0.196745046
## density -3.928077024 -0.464206872
## bark_wd_ratio -0.213919816 0.318956416
## mesh_sizeInvertebrates blocked:PC1 -0.119741770 -0.011384797
## mesh_sizeInvertebrates blocked:PC1_bark -0.124854175 -0.014902047
## mesh_sizeInvertebrates blocked:PC2_bark -0.134939883 -0.002975068
## mesh_sizeInvertebrates blocked:growth_formTrees -0.618466559 -0.035974366
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.263239319 0.111917094
## diameter_class5.0 cm:growth_formTrees -0.296772451 0.079350009
## mesh_sizeInvertebrates blocked:density 0.437350766 2.838332015## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.1622 6.1622 1 303.358 31.9571 3.637e-08 ***
## diameter_class 0.0005 0.0005 1 287.678 0.0024 0.96096
## growth_form 0.0076 0.0076 1 15.968 0.0397 0.84466
## PC1_bark 0.0336 0.0336 1 17.296 0.1743 0.68146
## PC2_bark 0.1146 0.1146 1 13.997 0.5945 0.45351
## PC1 0.2439 0.2439 1 14.069 1.2651 0.27951
## PC2 0.1878 0.1878 1 16.328 0.9737 0.33815
## density 0.4180 0.4180 1 42.303 2.1677 0.14834
## bark_wd_ratio 0.0367 0.0367 1 37.322 0.1904 0.66509
## mesh_size:PC1 1.0617 1.0617 1 303.358 5.5058 0.01960 *
## mesh_size:PC1_bark 1.1346 1.1346 1 303.358 5.8841 0.01586 *
## mesh_size:PC2_bark 0.7933 0.7933 1 303.358 4.1141 0.04340 *
## mesh_size:growth_form 0.8761 0.8761 1 303.358 4.5435 0.03385 *
## diameter_class:growth_form 0.2307 0.2307 1 312.123 1.1963 0.27490
## mesh_size:density 1.2594 1.2594 1 303.358 6.5313 0.01109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.202839208 0.413818376
## sd_(Intercept)|block 0.069254374 0.368354596
## sigma 0.401101115 0.469352444
## (Intercept) 3.323495061 5.200086592
## mesh_sizeInvertebrates blocked -2.307097796 -1.117466217
## diameter_class5.0 cm -0.082099644 0.184015897
## growth_formTrees -0.219323488 0.744026515
## PC1_bark -0.083439895 0.106047503
## PC2_bark -0.113268522 0.092597605
## PC1 0.002113429 0.171154277
## PC2 -0.052553994 0.196747250
## density -3.891505528 -0.430410097
## bark_wd_ratio -0.214105810 0.318936960
## mesh_sizeInvertebrates blocked:PC1 -0.119875245 -0.011409686
## mesh_sizeInvertebrates blocked:PC1_bark -0.123737853 -0.013811201
## mesh_sizeInvertebrates blocked:PC2_bark -0.135153245 -0.003057355
## mesh_sizeInvertebrates blocked:growth_formTrees -0.610921786 -0.028962228
## diameter_class5.0 cm:growth_formTrees -0.296969914 0.079525483
## mesh_sizeInvertebrates blocked:density 0.378420368 2.756186510## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.1622 6.1622 1 304.162 31.9511 3.64e-08 ***
## diameter_class 0.0004 0.0004 1 290.742 0.0022 0.96256
## growth_form 0.0133 0.0133 1 15.819 0.0691 0.79601
## PC1_bark 0.0241 0.0241 1 17.136 0.1252 0.72781
## PC2_bark 0.1128 0.1128 1 13.844 0.5849 0.45722
## PC1 0.2376 0.2376 1 13.916 1.2320 0.28583
## PC2 0.2034 0.2034 1 16.161 1.0546 0.31957
## density 0.4412 0.4412 1 42.263 2.2875 0.13786
## bark_wd_ratio 0.0783 0.0783 1 39.386 0.4060 0.52768
## mesh_size:PC1 1.0617 1.0617 1 304.162 5.5048 0.01961 *
## mesh_size:PC1_bark 1.1346 1.1346 1 304.162 5.8830 0.01587 *
## mesh_size:PC2_bark 0.7933 0.7933 1 304.162 4.1134 0.04342 *
## mesh_size:growth_form 0.8761 0.8761 1 304.162 4.5427 0.03386 *
## mesh_size:density 1.2594 1.2594 1 304.162 6.5301 0.01109 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.203357354 0.416141999
## sd_(Intercept)|block 0.069207488 0.368477524
## sigma 0.401821989 0.470219886
## (Intercept) 3.325475786 5.209260167
## mesh_sizeInvertebrates blocked -2.308183175 -1.116380839
## diameter_class5.0 cm -0.100449426 0.103215891
## growth_formTrees -0.255218598 0.703308480
## PC1_bark -0.080683402 0.109279874
## PC2_bark -0.113536414 0.093116774
## PC1 0.001392856 0.171074837
## PC2 -0.050012811 0.200090973
## density -3.932034160 -0.463308977
## bark_wd_ratio -0.187974420 0.342570287
## mesh_sizeInvertebrates blocked:PC1 -0.119974205 -0.011310725
## mesh_sizeInvertebrates blocked:PC1_bark -0.123838146 -0.013710908
## mesh_sizeInvertebrates blocked:PC2_bark -0.135273764 -0.002936836
## mesh_sizeInvertebrates blocked:growth_formTrees -0.611452745 -0.028431269
## mesh_sizeInvertebrates blocked:density 0.376250979 2.758355900## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.1622 6.1622 1 304.818 31.9147 3.696e-08 ***
## diameter_class 0.0015 0.0015 1 303.758 0.0076 0.93081
## growth_form 0.0010 0.0010 1 15.259 0.0054 0.94228
## PC1_bark 0.0632 0.0632 1 16.630 0.3276 0.57475
## PC2_bark 0.1228 0.1228 1 14.641 0.6360 0.43791
## PC1 0.2667 0.2667 1 14.690 1.3814 0.25856
## PC2 0.1760 0.1760 1 16.359 0.9117 0.35355
## density 0.4227 0.4227 1 40.601 2.1891 0.14671
## mesh_size:PC1 1.0617 1.0617 1 304.818 5.4985 0.01967 *
## mesh_size:PC1_bark 1.1346 1.1346 1 304.818 5.8763 0.01593 *
## mesh_size:PC2_bark 0.7933 0.7933 1 304.818 4.1087 0.04353 *
## mesh_size:growth_form 0.8761 0.8761 1 304.818 4.5375 0.03396 *
## mesh_size:density 1.2594 1.2594 1 304.818 6.5227 0.01114 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.201981689 0.410569768
## sd_(Intercept)|block 0.069143353 0.368131240
## sigma 0.402241899 0.470651821
## (Intercept) 3.466366547 5.242363658
## mesh_sizeInvertebrates blocked -2.308765052 -1.115798962
## diameter_class5.0 cm -0.103866420 0.095818095
## growth_formTrees -0.273083834 0.631127138
## PC1_bark -0.084240923 0.096428980
## PC2_bark -0.113010653 0.091958170
## PC1 0.003214338 0.171383346
## PC2 -0.054141871 0.188812736
## density -3.847797259 -0.421891020
## mesh_sizeInvertebrates blocked:PC1 -0.120027258 -0.011257672
## mesh_sizeInvertebrates blocked:PC1_bark -0.123891914 -0.013657140
## mesh_sizeInvertebrates blocked:PC2_bark -0.135338376 -0.002872228
## mesh_sizeInvertebrates blocked:growth_formTrees -0.611737395 -0.028146619
## mesh_sizeInvertebrates blocked:density 0.375087956 2.759518923## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.1622 6.1622 1 304.866 31.9210 3.685e-08 ***
## diameter_class 0.0099 0.0099 1 308.854 0.0514 0.82076
## growth_form 0.0228 0.0228 1 17.108 0.1182 0.73515
## PC1_bark 0.2082 0.2082 1 16.517 1.0786 0.31398
## PC2_bark 0.1719 0.1719 1 15.584 0.8905 0.35974
## PC1 0.3346 0.3346 1 15.578 1.7331 0.20706
## density 0.2851 0.2851 1 36.499 1.4769 0.23206
## mesh_size:PC1 1.0617 1.0617 1 304.866 5.4996 0.01966 *
## mesh_size:PC1_bark 1.1346 1.1346 1 304.866 5.8775 0.01592 *
## mesh_size:PC2_bark 0.7933 0.7933 1 304.866 4.1095 0.04351 *
## mesh_size:growth_form 0.8761 0.8761 1 304.866 4.5384 0.03394 *
## mesh_size:density 1.2594 1.2594 1 304.866 6.5240 0.01113 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] 524.7686## [1] 520.2054## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 6.1622 6.1622 1 305.781 32.0126 3.522e-08 ***
## growth_form 0.0276 0.0276 1 17.375 0.1432 0.70969
## PC1_bark 0.2013 0.2013 1 16.707 1.0459 0.32103
## PC2_bark 0.1732 0.1732 1 15.677 0.8999 0.35720
## PC1 0.3344 0.3344 1 15.670 1.7370 0.20646
## density 0.3780 0.3780 1 48.416 1.9636 0.16751
## mesh_size:PC1 1.0617 1.0617 1 305.781 5.5154 0.01949 *
## mesh_size:PC1_bark 1.1346 1.1346 1 305.781 5.8944 0.01577 *
## mesh_size:PC2_bark 0.7933 0.7933 1 305.781 4.1213 0.04321 *
## mesh_size:growth_form 0.8761 0.8761 1 305.781 4.5514 0.03369 *
## mesh_size:density 1.2594 1.2594 1 305.781 6.5427 0.01101 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## [1] 520.2054## [1] 514.1651## df AIC
## mod2 24 559.8641
## mod3 23 552.4305
## mod4 22 545.6784
## mod5 21 540.2025
## mod6a 20 536.2074
## mod6b 19 531.9867
## mod7 18 528.3625
## mod8 17 524.7686
## mod9 16 520.2054
## mod10 15 514.1651## R2m R2c
## [1,] 0.508308 0.7168813
## [1] 81 37## R2m R2c
## [1,] 0.508308 0.7168813## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k_trans ~ mesh_size + growth_form + PC1_bark + PC2_bark + PC1 +
## density + (1 | block) + (1 | species) + mesh_size:PC1 + mesh_size:PC1_bark +
## mesh_size:PC2_bark + mesh_size:growth_form + mesh_size:density
## Data: s
##
## REML criterion at convergence: 484.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3585 -0.5833 -0.0308 0.5432 2.9579
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.11728 0.3425
## block (Intercept) 0.02453 0.1566
## Residual 0.19249 0.4387
## Number of obs: 336, groups: species, 21; block, 4
##
## Fixed effects:
## Estimate Std. Error df
## (Intercept) 4.18038 0.43390 55.95652
## mesh_sizeInvertebrates blocked -1.71228 0.30656 305.78107
## growth_formTrees 0.24964 0.24853 21.00225
## PC1_bark -0.01200 0.04752 20.10330
## PC2_bark -0.01837 0.05832 18.72022
## PC1 0.09320 0.04790 18.71113
## density -1.87444 0.83655 62.95366
## mesh_sizeInvertebrates blocked:PC1 -0.06564 0.02795 305.78107
## mesh_sizeInvertebrates blocked:PC1_bark -0.06877 0.02833 305.78107
## mesh_sizeInvertebrates blocked:PC2_bark -0.06911 0.03404 305.78107
## mesh_sizeInvertebrates blocked:growth_formTrees -0.31994 0.14997 305.78107
## mesh_sizeInvertebrates blocked:density 1.56730 0.61274 305.78107
## t value Pr(>|t|)
## (Intercept) 9.635 1.75e-13 ***
## mesh_sizeInvertebrates blocked -5.585 5.15e-08 ***
## growth_formTrees 1.004 0.3266
## PC1_bark -0.253 0.8032
## PC2_bark -0.315 0.7563
## PC1 1.946 0.0668 .
## density -2.241 0.0286 *
## mesh_sizeInvertebrates blocked:PC1 -2.348 0.0195 *
## mesh_sizeInvertebrates blocked:PC1_bark -2.428 0.0158 *
## mesh_sizeInvertebrates blocked:PC2_bark -2.030 0.0432 *
## mesh_sizeInvertebrates blocked:growth_formTrees -2.133 0.0337 *
## mesh_sizeInvertebrates blocked:density 2.558 0.0110 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) msh_Ib grwt_T PC1_br PC2_br PC1 densty ms_Ib:PC1
## msh_szInvrb -0.353
## grwth_frmTr 0.083 -0.065
## PC1_bark 0.220 -0.089 0.228
## PC2_bark -0.169 0.045 0.507 -0.054
## PC1 -0.135 0.035 0.421 0.500 0.031
## density -0.931 0.353 -0.347 -0.275 0.030 0.020
## msh_sIb:PC1 0.042 -0.120 -0.122 -0.145 -0.009 -0.292 -0.009
## msh_Ib:PC1_ -0.105 0.297 -0.081 -0.298 0.017 -0.142 0.124 0.486
## msh_Ib:PC2_ 0.054 -0.153 -0.146 0.017 -0.292 -0.009 -0.014 0.031
## msh_szIb:_T -0.076 0.215 -0.302 -0.080 -0.141 -0.118 0.154 0.403
## msh_szIblc: 0.341 -0.965 0.127 0.101 -0.011 -0.007 -0.366 0.025
## m_Ib:PC1_ m_Ib:PC2 m_Ib:_
## msh_szInvrb
## grwth_frmTr
## PC1_bark
## PC2_bark
## PC1
## density
## msh_sIb:PC1
## msh_Ib:PC1_
## msh_Ib:PC2_ -0.057
## msh_szIb:_T 0.268 0.485
## msh_szIblc: -0.338 0.038 -0.422###
f1<- lmer(k_trans~ mesh_size+mesh_size:growth_form +mesh_size:diameter_class+
diameter_class+diameter_class:growth_form+
density+bark_wd_ratio+ (1|block)+ (1|species),data=s)
anova(f1)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## mesh_size 52.845 52.845 1 306.545 262.4545 <2e-16 ***
## diameter_class 0.001 0.001 1 309.456 0.0068 0.9346
## density 0.648 0.648 1 42.031 3.2186 0.0800 .
## bark_wd_ratio 0.119 0.119 1 44.971 0.5891 0.4468
## mesh_size:growth_form 0.506 0.253 2 42.920 1.2561 0.2950
## mesh_size:diameter_class 0.031 0.031 1 306.545 0.1536 0.6953
## growth_form:diameter_class 0.206 0.206 1 315.728 1.0255 0.3120
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.26059343 0.51329515
## sd_(Intercept)|block 0.06917762 0.37710062
## sigma 0.41189069 0.48196102
## (Intercept) 2.94387464 4.83460760
## mesh_sizeInvertebrates blocked -1.23939638 -0.91403862
## diameter_class5.0 cm -0.09200750 0.24071271
## density -3.16786021 0.09109904
## bark_wd_ratio -0.17299506 0.40415618
## mesh_sizeInvertebrates access:growth_formTrees -0.28652012 0.59354948
## mesh_sizeInvertebrates blocked:growth_formTrees -0.15024996 0.72981964
## mesh_sizeInvertebrates blocked:diameter_class5.0 cm -0.22913958 0.15237620
## growth_formTrees:diameter_class5.0 cm -0.29470869 0.09224962s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
s_fine_big_ln<- s_fine_big[s_fine_big$growth_form == "Lianas",]
s_fine_big_tr<- s_fine_big[s_fine_big$growth_form == "Trees",]
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_fine[s_crs$diameter_class == "5.0 cm",]
s_crs_big_ln<- s_crs_big[s_crs_big$growth_form == "Lianas",]
s_crs_big_tr<- s_crs_big[s_crs_big$growth_form == "Trees",]
hist(s_crs_big_ln$k)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_big_ln$k) + 2 ~ PC1 + (1 | species)
## Data: s_crs_big_ln
##
## REML criterion at convergence: 37.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1915 -0.6116 -0.1103 0.6399 2.0278
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.0242 0.1556
## Residual 0.1023 0.3199
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.96217 0.08417 9.00000 11.431 1.16e-06 ***
## PC1 0.08074 0.04522 9.00000 1.786 0.108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1 -0.601## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.32632 0.32632 1 9 3.1887 0.1078## 2.5 % 97.5 %
## sd_(Intercept)|species 0.00000000 0.2871984
## sigma 0.25587671 0.4155212
## (Intercept) 0.79891811 1.1254278
## PC1 -0.00695521 0.1684384## Analysis of Variance Table
##
## Response: log(s_crs_big_ln$k) + 2
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1 1 0.6350 0.63498 5.1591 0.02831 *
## Residuals 42 5.1693 0.12308
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = log(s_crs_big_ln$k) + 2 ~ PC1, data = s_crs_big_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.72719 -0.18644 -0.05837 0.24699 0.70468
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.96217 0.06617 14.540 <2e-16 ***
## PC1 0.08074 0.03555 2.271 0.0283 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3508 on 42 degrees of freedom
## Multiple R-squared: 0.1094, Adjusted R-squared: 0.08819
## F-statistic: 5.159 on 1 and 42 DF, p-value: 0.02831
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC1 + (1 | species)
## Data: s_crs_big_tr
##
## REML criterion at convergence: -20.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7554 -0.5943 -0.1596 0.4306 2.1238
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.02713 0.1647
## Residual 0.01751 0.1323
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.44957 0.06270 8.00000 7.170 9.52e-05 ***
## PC1 0.01756 0.02269 8.00000 0.774 0.461
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1 0.445## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.010484 0.010484 1 8 0.5988 0.4613## 2.5 % 97.5 %
## sd_(Intercept)|species 0.08355862 0.25680840
## sigma 0.10477729 0.17442674
## (Intercept) 0.32820865 0.57093817
## PC1 -0.02636494 0.06148464##
## Call:
## lm(formula = k ~ PC1, data = s_crs_big_tr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.26987 -0.14784 -0.02143 0.09259 0.52882
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.44957 0.03548 12.672 3.22e-15 ***
## PC1 0.01756 0.01284 1.368 0.179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2009 on 38 degrees of freedom
## Multiple R-squared: 0.04691, Adjusted R-squared: 0.02183
## F-statistic: 1.87 on 1 and 38 DF, p-value: 0.1795## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1 1 0.07548 0.075478 1.8702 0.1795
## Residuals 38 1.53361 0.040358
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_ln$k + 2) ~ PC1 + (1 | species)
## Data: s_fine_big_ln
##
## REML criterion at convergence: -107.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6961 -0.6461 -0.1799 0.5060 2.6002
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.000877 0.02961
## Residual 0.003166 0.05627
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.863141 0.015410 9.000000 56.012 9.27e-13 ***
## PC1 0.014698 0.008278 9.000000 1.776 0.11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1 -0.601## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.0099822 0.0099822 1 9 3.1526 0.1095## 2.5 % 97.5 %
## sd_(Intercept)|species 0.000000000 0.05317463
## sigma 0.045008516 0.07315347
## (Intercept) 0.833253167 0.89302862
## PC1 -0.001357199 0.03075282
##
## Call:
## lm(formula = log(s_fine_big_ln$k + 2) ~ PC1, data = s_fine_big_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09845 -0.03696 -0.01526 0.03544 0.16755
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.863141 0.011807 73.105 <2e-16 ***
## PC1 0.014698 0.006342 2.317 0.0254 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06259 on 42 degrees of freedom
## Multiple R-squared: 0.1134, Adjusted R-squared: 0.09226
## F-statistic: 5.37 on 1 and 42 DF, p-value: 0.02543## Analysis of Variance Table
##
## Response: log(s_fine_big_ln$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1 1 0.021041 0.0210413 5.3703 0.02543 *
## Residuals 42 0.164558 0.0039181
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_tr$k + 2) ~ PC1 + (1 | species)
## Data: s_fine_big_tr
##
## REML criterion at convergence: -91.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6997 -0.6318 -0.1420 0.5202 2.0062
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.004471 0.06686
## Residual 0.002691 0.05187
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.893498 0.025331 8.000000 35.273 4.57e-10 ***
## PC1 0.007924 0.009168 8.000000 0.864 0.413
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1 0.445## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.0020099 0.0020099 1 8 0.747 0.4126## 2.5 % 97.5 %
## sd_(Intercept)|species 0.034476831 0.10395126
## sigma 0.041074974 0.06837907
## (Intercept) 0.844465176 0.94252991
## PC1 -0.009822427 0.02566953## Analysis of Variance Table
##
## Response: log(s_fine_big_tr$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1 1 0.015368 0.0153681 2.3806 0.1311
## Residuals 38 0.245309 0.0064555s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_sm<-s_fine[s_fine$diameter_class == "2.5 cm",]
s_fine_sm_ln<- s_fine_sm[s_fine_sm$growth_form == "Lianas",]
s_fine_sm_tr<- s_fine_sm[s_fine_sm$growth_form == "Trees",]
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_sm<-s_fine[s_crs$diameter_class == "2.5 cm",]
s_crs_sm_ln<- s_crs_sm[s_crs_sm$growth_form == "Lianas",]
s_crs_sm_tr<- s_crs_sm[s_crs_sm$growth_form == "Trees",]
hist(s_crs_sm_ln$k)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_sm_ln$k) + 2 ~ PC1 + (1 | species)
## Data: s_crs_sm_ln
##
## REML criterion at convergence: 37.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.08195 -0.60448 -0.05343 0.62368 1.85141
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.20969 0.4579
## Residual 0.06716 0.2591
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.04847 0.17953 9.00000 5.840 0.000247 ***
## PC1 0.05231 0.09644 9.00000 0.542 0.600689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1 -0.601## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.01976 0.01976 1 9 0.2942 0.6007## 2.5 % 97.5 %
## sd_(Intercept)|species 0.2674729 0.6871611
## sigma 0.2072805 0.3368741
## (Intercept) 0.7002658 1.3966724
## PC1 -0.1347341 0.2393598## Analysis of Variance Table
##
## Response: log(s_crs_sm_ln$k) + 2
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1 1 0.2666 0.26655 1.0796 0.3047
## Residuals 42 10.3693 0.24689
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC1 + (1 | species)
## Data: s_crs_sm_tr
##
## REML criterion at convergence: -31.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9270 -0.5776 -0.1539 0.5737 1.7587
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.04646 0.2155
## Residual 0.01106 0.1052
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.48262 0.07836 8.00000 6.159 0.000271 ***
## PC1 0.01551 0.02836 8.00000 0.547 0.599375
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1 0.445## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.0033078 0.0033078 1 8 0.2991 0.5994## 2.5 % 97.5 %
## sd_(Intercept)|species 0.12438371 0.32719772
## sigma 0.08327577 0.13863234
## (Intercept) 0.33092915 0.63430493
## PC1 -0.03938876 0.07041014## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1 1 0.05889 0.058890 1.1735 0.2855
## Residuals 38 1.90699 0.050184
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_ln$k + 2) ~ PC1 + (1 | species)
## Data: s_fine_sm_ln
##
## REML criterion at convergence: -105.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2981 -0.6331 -0.1770 0.4276 2.5887
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.005470 0.07396
## Residual 0.002395 0.04894
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.88005 0.02939 9.00000 29.946 2.52e-10 ***
## PC1 0.01414 0.01579 9.00000 0.896 0.394
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1 -0.601## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.0019212 0.0019212 1 9 0.8023 0.3937## 2.5 % 97.5 %
## sd_(Intercept)|species 0.04188612 0.11182343
## sigma 0.03914139 0.06361291
## (Intercept) 0.82305637 0.93705120
## PC1 -0.01647763 0.04475781##
## Call:
## lm(formula = log(s_fine_sm_ln$k + 2) ~ PC1, data = s_fine_sm_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.16750 -0.04243 -0.01009 0.04211 0.20791
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.880054 0.015875 55.437 <2e-16 ***
## PC1 0.014140 0.008528 1.658 0.105
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08416 on 42 degrees of freedom
## Multiple R-squared: 0.06144, Adjusted R-squared: 0.0391
## F-statistic: 2.75 on 1 and 42 DF, p-value: 0.1047## Analysis of Variance Table
##
## Response: log(s_fine_sm_ln$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1 1 0.019475 0.0194747 2.7495 0.1047
## Residuals 42 0.297484 0.0070829
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_tr$k + 2) ~ PC1 + (1 | species)
## Data: s_fine_sm_tr
##
## REML criterion at convergence: -101
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9315 -0.5454 -0.1325 0.5638 1.6735
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.007420 0.08614
## Residual 0.001756 0.04190
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.906591 0.031311 7.999999 28.954 2.19e-09 ***
## PC1 0.007302 0.011332 7.999999 0.644 0.537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1 0.445## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1 0.00072892 0.00072892 1 8 0.4152 0.5374## 2.5 % 97.5 %
## sd_(Intercept)|species 0.04972521 0.13074402
## sigma 0.03317949 0.05523516
## (Intercept) 0.84598246 0.96719875
## PC1 -0.01463368 0.02923737##
## Call:
## lm(formula = log(s_fine_sm_tr$k + 2) ~ PC1, data = s_fine_sm_tr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14096 -0.06330 -0.03488 0.07679 0.18893
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.906591 0.015799 57.381 <2e-16 ***
## PC1 0.007302 0.005718 1.277 0.209
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08946 on 38 degrees of freedom
## Multiple R-squared: 0.04114, Adjusted R-squared: 0.01591
## F-statistic: 1.631 on 1 and 38 DF, p-value: 0.2094## Bark
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
s_fine_big_ln<- s_fine_big[s_fine_big$growth_form == "Lianas",]
s_fine_big_tr<- s_fine_big[s_fine_big$growth_form == "Trees",]
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_fine[s_crs$diameter_class == "5.0 cm",]
s_crs_big_ln<- s_crs_big[s_crs_big$growth_form == "Lianas",]
s_crs_big_tr<- s_crs_big[s_crs_big$growth_form == "Trees",]
hist(s_crs_big_ln$k)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_big_ln$k) + 2 ~ PC1_bark + (1 | species)
## Data: s_crs_big_ln
##
## REML criterion at convergence: 37.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1002 -0.6450 -0.1749 0.5545 1.9021
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.01783 0.1335
## Residual 0.10234 0.3199
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.03975 0.06309 9.00000 16.482 4.97e-08 ***
## PC1_bark -0.06424 0.02880 9.00000 -2.231 0.0526 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1_bark 0.091## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.50922 0.50922 1 9 4.976 0.05264 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.0000000 0.262336003
## sigma 0.2558767 0.413150896
## (Intercept) 0.9173965 1.162105954
## PC1_bark -0.1200924 -0.008385257## Analysis of Variance Table
##
## Response: log(s_crs_big_ln$k) + 2
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1_bark 1 0.8642 0.86418 7.347 0.009686 **
## Residuals 42 4.9401 0.11762
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = log(s_crs_big_ln$k) + 2 ~ PC1_bark, data = s_crs_big_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67079 -0.24635 -0.07185 0.18963 0.66451
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.03975 0.05192 20.027 < 2e-16 ***
## PC1_bark -0.06424 0.02370 -2.711 0.00969 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.343 on 42 degrees of freedom
## Multiple R-squared: 0.1489, Adjusted R-squared: 0.1286
## F-statistic: 7.347 on 1 and 42 DF, p-value: 0.009686
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC1_bark + (1 | species)
## Data: s_crs_big_tr
##
## REML criterion at convergence: -21.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7606 -0.5788 -0.1482 0.4355 2.1186
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.02616 0.1618
## Residual 0.01751 0.1323
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.43348 0.05558 8.00000 7.799 5.24e-05 ***
## PC1_bark -0.02525 0.02704 8.00000 -0.934 0.378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1_bark -0.106## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.015266 0.015266 1 8 0.8719 0.3777## 2.5 % 97.5 %
## sd_(Intercept)|species 0.08128770 0.25256137
## sigma 0.10477729 0.17442674
## (Intercept) 0.32589454 0.54106188
## PC1_bark -0.07758669 0.02709029## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1_bark 1 0.10652 0.106521 2.6939 0.109
## Residuals 38 1.50257 0.039541
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_ln$k + 2) ~ PC1_bark + (1 | species)
## Data: s_fine_big_ln
##
## REML criterion at convergence: -107.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6088 -0.6805 -0.2214 0.4354 2.5817
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.0007327 0.02707
## Residual 0.0031664 0.05627
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.877362 0.011820 9.000000 74.226 7.4e-14 ***
## PC1_bark -0.011193 0.005396 9.000000 -2.074 0.0679 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1_bark 0.091## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.013625 0.013625 1 9 4.3032 0.06788 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.00000000 0.0501748402
## sigma 0.04500852 0.0730753022
## (Intercept) 0.85443691 0.9002877220
## PC1_bark -0.02165827 -0.0007278877## Analysis of Variance Table
##
## Response: log(s_fine_big_ln$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1_bark 1 0.026237 0.0262365 6.9146 0.0119 *
## Residuals 42 0.159363 0.0037944
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = log(s_fine_big_ln$k + 2) ~ PC1_bark, data = s_fine_big_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10378 -0.04586 -0.02014 0.02483 0.16205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.877362 0.009325 94.09 <2e-16 ***
## PC1_bark -0.011193 0.004257 -2.63 0.0119 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0616 on 42 degrees of freedom
## Multiple R-squared: 0.1414, Adjusted R-squared: 0.1209
## F-statistic: 6.915 on 1 and 42 DF, p-value: 0.0119
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_tr$k + 2) ~ PC1_bark + (1 | species)
## Data: s_fine_big_tr
##
## REML criterion at convergence: -92.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7064 -0.6015 -0.1244 0.5276 1.9994
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.004305 0.06561
## Residual 0.002691 0.05187
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.88618 0.02244 8.00000 39.494 1.86e-10 ***
## PC1_bark -0.01112 0.01092 8.00000 -1.019 0.338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1_bark -0.106## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.0027924 0.0027924 1 8 1.0378 0.3381## 2.5 % 97.5 %
## sd_(Intercept)|species 0.03354177 0.10216041
## sigma 0.04107497 0.06837907
## (Intercept) 0.84274201 0.92960882
## PC1_bark -0.03225050 0.01000943## Analysis of Variance Table
##
## Response: log(s_fine_big_tr$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1_bark 1 0.020665 0.0206646 3.2717 0.0784 .
## Residuals 38 0.240012 0.0063161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_sm<-s_fine[s_fine$diameter_class == "2.5 cm",]
s_fine_sm_ln<- s_fine_sm[s_fine_sm$growth_form == "Lianas",]
s_fine_sm_tr<- s_fine_sm[s_fine_sm$growth_form == "Trees",]
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_sm<-s_fine[s_crs$diameter_class == "2.5 cm",]
s_crs_sm_ln<- s_crs_sm[s_crs_sm$growth_form == "Lianas",]
s_crs_sm_tr<- s_crs_sm[s_crs_sm$growth_form == "Trees",]
hist(s_crs_sm_ln$k)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_sm_ln$k) + 2 ~ PC1_bark + (1 | species)
## Data: s_crs_sm_ln
##
## REML criterion at convergence: 33.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1983 -0.6303 -0.1213 0.4929 1.9400
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.11224 0.3350
## Residual 0.06716 0.2591
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.08033 0.10875 9.00000 9.934 3.78e-06 ***
## PC1_bark -0.13426 0.04964 9.00000 -2.704 0.0242 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1_bark 0.091## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.49119 0.49119 1 9 7.3141 0.02422 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.1809177 0.51171734
## sigma 0.2072805 0.33687406
## (Intercept) 0.8694090 1.29125625
## PC1_bark -0.2305437 -0.03797516## Analysis of Variance Table
##
## Response: log(s_crs_sm_ln$k) + 2
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1_bark 1 3.7748 3.7748 23.108 1.987e-05 ***
## Residuals 42 6.8611 0.1634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = log(s_crs_sm_ln$k) + 2 ~ PC1_bark, data = s_crs_sm_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.14699 -0.17848 0.03844 0.18085 0.80117
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.08033 0.06118 17.657 < 2e-16 ***
## PC1_bark -0.13426 0.02793 -4.807 1.99e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4042 on 42 degrees of freedom
## Multiple R-squared: 0.3549, Adjusted R-squared: 0.3396
## F-statistic: 23.11 on 1 and 42 DF, p-value: 1.987e-05
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC1_bark + (1 | species)
## Data: s_crs_sm_tr
##
## REML criterion at convergence: -31.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9701 -0.5715 -0.1520 0.5708 1.7646
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.04379 0.2093
## Residual 0.01106 0.1052
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.46995 0.06862 8.00000 6.848 0.000131 ***
## PC1_bark -0.02938 0.03338 8.00000 -0.880 0.404529
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1_bark -0.106## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.0085642 0.0085642 1 8 0.7744 0.4045## 2.5 % 97.5 %
## sd_(Intercept)|species 0.12030597 0.31797866
## sigma 0.08327577 0.13863234
## (Intercept) 0.33711417 0.60277777
## PC1_bark -0.09399896 0.03524399## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1_bark 1 0.14421 0.144213 3.0083 0.09095 .
## Residuals 38 1.82167 0.047939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_ln$k + 2) ~ PC1_bark + (1 | species)
## Data: s_fine_sm_ln
##
## REML criterion at convergence: -109
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.2624 -0.6366 -0.1547 0.2682 2.6553
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.002990 0.05468
## Residual 0.002395 0.04894
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.891348 0.018136 8.999999 49.147 3e-12 ***
## PC1_bark -0.022788 0.008279 8.999999 -2.753 0.0224 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1_bark 0.091## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.018143 0.018143 1 9 7.5764 0.02238 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.02750143 0.084574355
## sigma 0.03914139 0.063612911
## (Intercept) 0.85617256 0.926523814
## PC1_bark -0.03884554 -0.006730972## Analysis of Variance Table
##
## Response: log(s_fine_sm_ln$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1_bark 1 0.10875 0.108750 21.937 2.952e-05 ***
## Residuals 42 0.20821 0.004957
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = log(s_fine_sm_ln$k + 2) ~ PC1_bark, data = s_fine_sm_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14260 -0.03119 -0.00417 0.02615 0.19062
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.891348 0.010658 83.629 < 2e-16 ***
## PC1_bark -0.022788 0.004865 -4.684 2.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07041 on 42 degrees of freedom
## Multiple R-squared: 0.3431, Adjusted R-squared: 0.3275
## F-statistic: 21.94 on 1 and 42 DF, p-value: 2.952e-05
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_tr$k + 2) ~ PC1_bark + (1 | species)
## Data: s_fine_sm_tr
##
## REML criterion at convergence: -101.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9776 -0.5416 -0.1401 0.5680 1.6274
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.006976 0.08352
## Residual 0.001756 0.04190
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.90039 0.02738 8.00000 32.879 7.99e-10 ***
## PC1_bark -0.01277 0.01332 8.00000 -0.959 0.366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC1_bark -0.106## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC1_bark 0.0016139 0.0016139 1 8 0.9192 0.3658## 2.5 % 97.5 %
## sd_(Intercept)|species 0.04802512 0.12689879
## sigma 0.03317949 0.05523516
## (Intercept) 0.84738650 0.95340285
## PC1_bark -0.03856108 0.01301492## Analysis of Variance Table
##
## Response: log(s_fine_sm_tr$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC1_bark 1 0.027263 0.0272626 3.5731 0.06637 .
## Residuals 38 0.289935 0.0076299
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 112.1 Plotting
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 11.34,"%", " P = 0.02"))
fine_big_plot<- ggplot()+ geom_point(data=s_fine_big, aes(y=k, x=PC1,color=growth_form,alpha = 0.6), size=3)+
geom_smooth(method = "lm",size=1, data=s_fine_big, aes(y=k, x=PC1, colour=growth_form,linetype=growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_y_continuous(limits = c(0,1.5))+
labs(colour ="Growth forms", title = "(b) Inverterbrates blocked", y= "Decay rate (k) 4.0 cm WD)",
x= "Wood traits PC1")+
annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
fine_big_plot
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_small<-s_fine[s_fine$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.16,"%", " P < 0.001"))
lb1b <- expression(paste("", R^2 , "= ", 9.96,"%", " P = 0.014"))
fine_small_plot<- ggplot()+ geom_point(data=s_fine_small, aes(y=k, x=PC1, colour=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_fine_small, aes(y=k, x=PC1, colour=growth_form, linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+scale_y_continuous(limits = c(0,1.5))+
labs(colour ="Growth forms", title = "(a) Inverterbrates blocked", y= " Decay rate (k) 2.5 cm WD)",
x= "Wood traits PC1")+ guides(shape=FALSE)+
annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = c(0.3,0.8),
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
fine_small_plot
###
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_crs[s_crs$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 10.94,"%", " P = 0.03"))
crs_big_plot<- ggplot()+ geom_point(data=s_crs_big, aes(y=k, x=PC1, colour=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_crs_big, aes(y=k, x=PC1, colour=growth_form,linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_y_continuous(limits = c(0,5))+
annotate("text", x = -4, y = 4, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
labs(colour ="Growth forms", title = " (d) Inverterbrates access", y= "Decay rate (k) 4.0 cm WD)",
x= "Wood traits PC1")+ guides(shape=FALSE)+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
crs_big_plot
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_small<-s_crs[s_crs$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.16,"%", " P < 0.001"))
lb1b <- expression(paste("", R^2 , "= ", 9.96,"%", " P = 0.014"))
crs_small_plot<- ggplot()+ geom_point(data=s_crs_small, aes(y=k, x=PC1, colour =growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_crs_small, aes(y=k, x=PC1, colour=growth_form, linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_y_continuous(limits = c(0,5))+
labs(colour ="Growth forms", title = "(c) Inverterbrates access", y= " Decay rate (k) 2.5 cm WD)",
x= "Wood traits PC1")+ guides(shape=FALSE)+
annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
#crs_small_plot
#plt_pca<- fine_small_plot+fine_big_plot+crs_small_plot+crs_big_plot
#ggsave(filename="plt_pca_k.png", plot=plt_pca, device="png",
# path=path, height=6, width=6, units="in", dpi=500)
###
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big <- s_fine[s_fine$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 14.14,"%", " P = 0.01"))
fine_big_plot_brk<- ggplot()+ geom_point(data=s_fine_big, aes(y=k, x=PC1_bark*-1,color=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_fine_big, aes(y=k, x=PC1_bark*-1, colour=growth_form,linetype=growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
scale_y_continuous(limits = c(0,1.5))+
labs(colour ="Growth forms", title = "(f) Inverterbrates blocked", y= "Decay rate (k) 4.0 cm WD)",
x= "Bark traits PC1")+ guides(shape=FALSE)+
annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
fine_big_plot_brk
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_small<-s_fine[s_fine$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 34.31,"%", " P < 0.001"))
fine_small_plot_brk<- ggplot()+ geom_point(data=s_fine_small, aes(y=k, x=PC1_bark*-1, colour=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_fine_small, aes(y=k, x=PC1_bark*-1, colour=growth_form, linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+scale_y_continuous(limits = c(0,1.5))+
annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
labs(colour ="Growth forms", title = "(e) Inverterbrates blocked", y= " Decay rate (k) 2.5 cm WD)",
x= "Bark traits PC1")+ guides(shape=FALSE)+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
fine_small_plot_brk
###
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_crs[s_crs$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 7.92,"%", " P = 0.01"))
crs_big_plot_brk<- ggplot()+ geom_point(data=s_crs_big, aes(y=k, x=PC1_bark*-1, colour=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_crs_big, aes(y=k, x=PC1_bark*-1, colour=growth_form,linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
annotate("text", x = 0, y = 4, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
scale_y_continuous(limits = c(0,5))+
labs(colour ="Growth forms", title = "(h) Inverterbrates access", y= "Decay rate (k) 4.0 cm WD)",
x= "Bark traits PC1")+ guides(shape=FALSE)+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
crs_big_plot_brk
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_small<-s_crs[s_crs$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.49,"%", " P < 0.001"))
crs_small_plot_brk<- ggplot()+ geom_point(data=s_crs_small, aes(y=k, x=PC1_bark*-1, colour =growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_crs_small, aes(y=k, x=PC1_bark*-1, colour=growth_form, linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 1, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
annotate("text", x = -1.5, y = 4, size=3, label = as.character(lb1),parse=TRUE, colour= "lightcoral") +
scale_y_continuous(limits = c(0,5))+
labs(colour ="Growth forms", title = "(g) Inverterbrates access", y= " Decay rate (k) 2.5 cm WD)",
x= "Bark traits PC1")+ guides(shape=FALSE)+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
crs_small_plot_brk
plt_pca_brk<- fine_small_plot_brk+fine_big_plot_brk+crs_small_plot_brk+crs_big_plot_brk
#ggsave(filename="plt_pca_k.png", plot=plt_pca, device="png",
# path=path, height=6, width=6, units="in", dpi=500)
plt_pca_wd_brk<- fine_small_plot+fine_big_plot+crs_small_plot+crs_big_plot+
fine_small_plot_brk+fine_big_plot_brk+crs_small_plot_brk+crs_big_plot_brk+plot_layout(nrow = 2)
## Plot figure S7 for wood and bark traits with PC1
ggsave(filename="Figure S7.png", plot=plt_pca_wd_brk, device="png",
path=path, height=6, width=12, units="in", dpi=500)12.2 PC2
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_big_ln$k) + 2 ~ PC2 + (1 | species)
## Data: s_crs_big_ln
##
## REML criterion at convergence: 39.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.16267 -0.75417 -0.00489 0.57337 2.04125
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.04146 0.2036
## Residual 0.10234 0.3199
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.06461 0.09463 9.00000 11.251 1.33e-06 ***
## PC2 0.01785 0.07889 9.00000 0.226 0.826
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2 0.565## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0052368 0.0052368 1 9 0.0512 0.8261## 2.5 % 97.5 %
## sd_(Intercept)|species 0.0000000 0.3458207
## sigma 0.2558767 0.4158519
## (Intercept) 0.8810801 1.2481326
## PC2 -0.1351642 0.1708568## Analysis of Variance Table
##
## Response: log(s_crs_big_ln$k) + 2
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2 1 0.0137 0.013723 0.0995 0.754
## Residuals 42 5.7906 0.137871##
## Call:
## lm(formula = log(s_crs_big_ln$k) + 2 ~ PC2, data = s_crs_big_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.72170 -0.25143 -0.04267 0.22986 0.90193
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.06461 0.06785 15.691 <2e-16 ***
## PC2 0.01785 0.05657 0.315 0.754
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3713 on 42 degrees of freedom
## Multiple R-squared: 0.002364, Adjusted R-squared: -0.02139
## F-statistic: 0.09953 on 1 and 42 DF, p-value: 0.754
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC2 + (1 | species)
## Data: s_crs_big_tr
##
## REML criterion at convergence: -22.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7820 -0.6277 -0.1360 0.4831 2.0972
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.02571 0.1603
## Residual 0.01751 0.1323
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.40301 0.06022 8.00000 6.692 0.000154 ***
## PC2 0.03346 0.03336 8.00000 1.003 0.345121
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2 -0.413## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.017622 0.017622 1 8 1.0065 0.3451## 2.5 % 97.5 %
## sd_(Intercept)|species 0.08019561 0.25053900
## sigma 0.10477729 0.17442674
## (Intercept) 0.28643754 0.51958794
## PC2 -0.03110173 0.09802825##
## Call:
## lm(formula = k ~ PC2, data = s_crs_big_tr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.3147 -0.1403 -0.0481 0.1028 0.4916
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.40301 0.03435 11.731 3.37e-14 ***
## PC2 0.03346 0.01903 1.759 0.0867 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1979 on 38 degrees of freedom
## Multiple R-squared: 0.07527, Adjusted R-squared: 0.05094
## F-statistic: 3.093 on 1 and 38 DF, p-value: 0.08667## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2 1 0.12112 0.121122 3.0932 0.08667 .
## Residuals 38 1.48797 0.039157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_ln$k + 2) ~ PC2 + (1 | species)
## Data: s_fine_big_ln
##
## REML criterion at convergence: -105.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6752 -0.7272 -0.1130 0.4444 2.7370
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.001453 0.03812
## Residual 0.003166 0.05627
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.881391 0.017314 9.000000 50.906 2.19e-12 ***
## PC2 0.002664 0.014435 9.000000 0.185 0.858
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2 0.565## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.00010788 0.00010788 1 9 0.0341 0.8576## 2.5 % 97.5 %
## sd_(Intercept)|species 0.00000000 0.06377139
## sigma 0.04500852 0.07314859
## (Intercept) 0.84781055 0.91497229
## PC2 -0.02533276 0.03066170
##
## Call:
## lm(formula = log(s_fine_big_ln$k + 2) ~ PC2, data = s_fine_big_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09709 -0.04587 -0.01591 0.03182 0.20331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.881391 0.012137 72.620 <2e-16 ***
## PC2 0.002664 0.010119 0.263 0.794
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06642 on 42 degrees of freedom
## Multiple R-squared: 0.001648, Adjusted R-squared: -0.02212
## F-statistic: 0.06934 on 1 and 42 DF, p-value: 0.7936## Analysis of Variance Table
##
## Response: log(s_fine_big_ln$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2 1 0.000306 0.0003059 0.0693 0.7936
## Residuals 42 0.185294 0.0044118## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_tr$k + 2) ~ PC2 + (1 | species)
## Data: s_fine_big_tr
##
## REML criterion at convergence: -92.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7300 -0.6498 -0.1169 0.5763 1.9759
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.004243 0.06514
## Residual 0.002691 0.05187
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.87295 0.02434 8.00000 35.861 4e-10 ***
## PC2 0.01448 0.01348 8.00000 1.074 0.314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2 -0.413## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0031023 0.0031023 1 8 1.153 0.3142## 2.5 % 97.5 %
## sd_(Intercept)|species 0.03318043 0.10147438
## sigma 0.04107497 0.06837907
## (Intercept) 0.82583259 0.92007192
## PC2 -0.01162028 0.04057403## Analysis of Variance Table
##
## Response: log(s_fine_big_tr$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2 1 0.022669 0.0226692 3.6193 0.06471 .
## Residuals 38 0.238007 0.0062634
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_sm<-s_fine[s_fine$diameter_class == "2.5 cm",]
s_fine_sm_ln<- s_fine_sm[s_fine_sm$growth_form == "Lianas",]
s_fine_sm_tr<- s_fine_sm[s_fine_sm$growth_form == "Trees",]
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_sm<-s_fine[s_crs$diameter_class == "2.5 cm",]
s_crs_sm_ln<- s_crs_sm[s_crs_sm$growth_form == "Lianas",]
s_crs_sm_tr<- s_crs_sm[s_crs_sm$growth_form == "Trees",]
hist(s_crs_sm_ln$k)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_sm_ln$k) + 2 ~ PC2 + (1 | species)
## Data: s_crs_sm_ln
##
## REML criterion at convergence: 36.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.08188 -0.58575 -0.04757 0.61618 1.86929
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.21470 0.4634
## Residual 0.06716 0.2591
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.13733 0.17583 9.00000 6.468 0.000116 ***
## PC2 0.04476 0.14659 9.00000 0.305 0.767081
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2 0.565## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0062595 0.0062595 1 9 0.0932 0.7671## 2.5 % 97.5 %
## sd_(Intercept)|species 0.2711511 0.6949806
## sigma 0.2072805 0.3368741
## (Intercept) 0.7963078 1.4783599
## PC2 -0.2395667 0.3290775## Analysis of Variance Table
##
## Response: log(s_crs_sm_ln$k) + 2
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2 1 0.0863 0.086305 0.3436 0.5609
## Residuals 42 10.5496 0.251181
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC2 + (1 | species)
## Data: s_crs_sm_tr
##
## REML criterion at convergence: -32.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9327 -0.6113 -0.1780 0.5863 1.7615
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.04342 0.2084
## Residual 0.01106 0.1052
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.43520 0.07462 8.00000 5.832 0.000391 ***
## PC2 0.03800 0.04133 8.00000 0.919 0.384773
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2 -0.413## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0093485 0.0093485 1 8 0.8453 0.3848## 2.5 % 97.5 %
## sd_(Intercept)|species 0.11972373 0.3166665
## sigma 0.08327577 0.1386323
## (Intercept) 0.29076041 0.5796369
## PC2 -0.04200065 0.1179932## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2 1 0.15616 0.156160 3.279 0.07808 .
## Residuals 38 1.80972 0.047624
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_ln$k + 2) ~ PC2 + (1 | species)
## Data: s_fine_sm_ln
##
## REML criterion at convergence: -105.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3079 -0.6337 -0.1672 0.4170 2.5789
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.005984 0.07736
## Residual 0.002395 0.04894
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.899079 0.029650 8.999999 30.323 2.26e-10 ***
## PC2 0.004729 0.024720 8.999999 0.191 0.853
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2 0.565## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2 8.7622e-05 8.7622e-05 1 9 0.0366 0.8525## 2.5 % 97.5 %
## sd_(Intercept)|species 0.04426066 0.11668043
## sigma 0.03914139 0.06361291
## (Intercept) 0.84157206 0.95658677
## PC2 -0.04321666 0.05267404##
## Call:
## lm(formula = log(s_fine_sm_ln$k + 2) ~ PC2, data = s_fine_sm_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.147521 -0.061555 -0.009852 0.036437 0.232543
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.899079 0.015850 56.725 <2e-16 ***
## PC2 0.004729 0.013214 0.358 0.722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08674 on 42 degrees of freedom
## Multiple R-squared: 0.00304, Adjusted R-squared: -0.0207
## F-statistic: 0.1281 on 1 and 42 DF, p-value: 0.7223## Analysis of Variance Table
##
## Response: log(s_fine_sm_ln$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2 1 0.000963 0.0009634 0.1281 0.7223
## Residuals 42 0.315995 0.0075237
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_tr$k + 2) ~ PC2 + (1 | species)
## Data: s_fine_sm_tr
##
## REML criterion at convergence: -102.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9368 -0.5828 -0.1616 0.5779 1.6682
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.006923 0.08321
## Residual 0.001756 0.04190
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.88541 0.02979 8.00000 29.719 1.78e-09 ***
## PC2 0.01635 0.01650 8.00000 0.991 0.351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2 -0.413## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2 0.0017248 0.0017248 1 8 0.9824 0.3506## 2.5 % 97.5 %
## sd_(Intercept)|species 0.04782143 0.12643956
## sigma 0.03317949 0.05523516
## (Intercept) 0.82774197 0.94307890
## PC2 -0.01558490 0.04829427##
## Call:
## lm(formula = log(s_fine_sm_tr$k + 2) ~ PC2, data = s_fine_sm_tr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.14553 -0.06887 -0.01528 0.07326 0.18178
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.885410 0.015121 58.556 <2e-16 ***
## PC2 0.016355 0.008375 1.953 0.0582 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0871 on 38 degrees of freedom
## Multiple R-squared: 0.09121, Adjusted R-squared: 0.06729
## F-statistic: 3.814 on 1 and 38 DF, p-value: 0.05822
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_big_ln$k) + 2 ~ PC2_bark + (1 | species)
## Data: s_crs_big_ln
##
## REML criterion at convergence: 40.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1519 -0.7420 -0.0156 0.5978 2.0459
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.04172 0.2043
## Residual 0.10234 0.3199
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.060545 0.101315 9.000000 10.468 2.44e-06 ***
## PC2_bark -0.007902 0.063321 9.000000 -0.125 0.903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2_bark -0.636## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 0.0015937 0.0015937 1 9 0.0156 0.9034## 2.5 % 97.5 %
## sd_(Intercept)|species 0.0000000 0.3466436
## sigma 0.2558767 0.4158524
## (Intercept) 0.8640442 1.2570466
## PC2_bark -0.1307143 0.1149105## Analysis of Variance Table
##
## Response: log(s_crs_big_ln$k) + 2
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2_bark 1 0.0042 0.004193 0.0304 0.8625
## Residuals 42 5.8001 0.138098##
## Call:
## lm(formula = log(s_fine_sm_tr$k + 2) ~ PC1_bark, data = s_fine_sm_tr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.13071 -0.07394 -0.02066 0.07216 0.18521
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.900395 0.013890 64.82 <2e-16 ***
## PC1_bark -0.012773 0.006757 -1.89 0.0664 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08735 on 38 degrees of freedom
## Multiple R-squared: 0.08595, Adjusted R-squared: 0.06189
## F-statistic: 3.573 on 1 and 38 DF, p-value: 0.06637
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC2_bark + (1 | species)
## Data: s_crs_big_tr
##
## REML criterion at convergence: -24.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6831 -0.5714 -0.1628 0.4073 2.1960
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.01750 0.1323
## Residual 0.01751 0.1323
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.35877 0.05727 8.00000 6.265 0.000242 ***
## PC2_bark -0.06186 0.02954 8.00000 -2.094 0.069575 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2_bark 0.577## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 0.076776 0.076776 1 8 4.3852 0.06957 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.05666082 0.210881931
## sigma 0.10477729 0.174426743
## (Intercept) 0.24791832 0.469621207
## PC2_bark -0.11903836 -0.004679129## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2_bark 1 0.38376 0.38376 11.901 0.001388 **
## Residuals 38 1.22534 0.03225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = k ~ PC2_bark, data = s_crs_big_tr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.25910 -0.12560 -0.02392 0.09191 0.48863
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.35877 0.03476 10.32 1.41e-12 ***
## PC2_bark -0.06186 0.01793 -3.45 0.00139 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1796 on 38 degrees of freedom
## Multiple R-squared: 0.2385, Adjusted R-squared: 0.2185
## F-statistic: 11.9 on 1 and 38 DF, p-value: 0.001388
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_ln$k + 2) ~ PC2_bark + (1 | species)
## Data: s_fine_big_ln
##
## REML criterion at convergence: -105.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6663 -0.7256 -0.1201 0.4657 2.7378
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.001460 0.03821
## Residual 0.003166 0.05627
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.8804219 0.0185315 9.0000000 47.509 4.06e-12 ***
## PC2_bark -0.0008226 0.0115821 9.0000000 -0.071 0.945
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2_bark -0.636## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 1.5972e-05 1.5972e-05 1 9 0.005 0.9449## 2.5 % 97.5 %
## sd_(Intercept)|species 0.00000000 0.06389343
## sigma 0.04500852 0.07314857
## (Intercept) 0.84447994 0.91636391
## PC2_bark -0.02328618 0.02164100## Analysis of Variance Table
##
## Response: log(s_fine_big_ln$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2_bark 1 0.000045 0.0000454 0.0103 0.9197
## Residuals 42 0.185554 0.0044180##
## Call:
## lm(formula = log(s_fine_big_ln$k + 2) ~ PC2_bark, data = s_fine_big_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09568 -0.04591 -0.01463 0.03206 0.20369
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8804219 0.0129786 67.837 <2e-16 ***
## PC2_bark -0.0008226 0.0081116 -0.101 0.92
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.06647 on 42 degrees of freedom
## Multiple R-squared: 0.0002448, Adjusted R-squared: -0.02356
## F-statistic: 0.01028 on 1 and 42 DF, p-value: 0.9197
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_big_tr$k + 2) ~ PC2_bark + (1 | species)
## Data: s_fine_big_tr
##
## REML criterion at convergence: -95.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.6456 -0.6114 -0.1353 0.4447 2.0602
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.002997 0.05474
## Residual 0.002691 0.05187
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.85581 0.02345 8.00000 36.491 3.49e-10 ***
## PC2_bark -0.02497 0.01210 8.00000 -2.064 0.0729 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2_bark 0.577## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 0.011466 0.011466 1 8 4.2613 0.07287 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.02480846 0.086710674
## sigma 0.04107497 0.068379073
## (Intercept) 0.81041610 0.901209676
## PC2_bark -0.04838904 -0.001555721## Analysis of Variance Table
##
## Response: log(s_fine_big_tr$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2_bark 1 0.062542 0.062542 11.995 0.001336 **
## Residuals 38 0.198135 0.005214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = log(s_fine_big_tr$k + 2) ~ PC2_bark, data = s_fine_big_tr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.111608 -0.051016 -0.006749 0.040043 0.185531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.85581 0.01398 61.223 < 2e-16 ***
## PC2_bark -0.02497 0.00721 -3.463 0.00134 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07221 on 38 degrees of freedom
## Multiple R-squared: 0.2399, Adjusted R-squared: 0.2199
## F-statistic: 11.99 on 1 and 38 DF, p-value: 0.001336s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_sm<-s_fine[s_fine$diameter_class == "2.5 cm",]
s_fine_sm_ln<- s_fine_sm[s_fine_sm$growth_form == "Lianas",]
s_fine_sm_tr<- s_fine_sm[s_fine_sm$growth_form == "Trees",]
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_sm<-s_fine[s_crs$diameter_class == "2.5 cm",]
s_crs_sm_ln<- s_crs_sm[s_crs_sm$growth_form == "Lianas",]
s_crs_sm_tr<- s_crs_sm[s_crs_sm$growth_form == "Trees",]
hist(s_crs_sm_ln$k)
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_crs_sm_ln$k) + 2 ~ PC2_bark + (1 | species)
## Data: s_crs_sm_ln
##
## REML criterion at convergence: 37.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.07220 -0.58898 -0.05048 0.62801 1.85966
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.21709 0.4659
## Residual 0.06716 0.2591
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.1075672 0.1888618 8.9999993 5.864 0.000239 ***
## PC2_bark -0.0005588 0.1180378 8.9999993 -0.005 0.996326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2_bark -0.636## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 1.5051e-06 1.5051e-06 1 9 0 0.9963## 2.5 % 97.5 %
## sd_(Intercept)|species 0.2728939 0.6986928
## sigma 0.2072805 0.3368741
## (Intercept) 0.7412683 1.4738662
## PC2_bark -0.2294941 0.2283765## Analysis of Variance Table
##
## Response: log(s_crs_sm_ln$k) + 2
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2_bark 1 0.000 0.000021 1e-04 0.9928
## Residuals 42 10.636 0.253235##
## Call:
## lm(formula = log(s_crs_sm_ln$k) + 2 ~ PC2_bark, data = s_crs_sm_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.23097 -0.31997 0.05289 0.29591 0.97730
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1075672 0.0982604 11.272 2.79e-14 ***
## PC2_bark -0.0005588 0.0614123 -0.009 0.993
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5032 on 42 degrees of freedom
## Multiple R-squared: 1.971e-06, Adjusted R-squared: -0.02381
## F-statistic: 8.28e-05 on 1 and 42 DF, p-value: 0.9928
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: k ~ PC2_bark + (1 | species)
## Data: s_crs_sm_tr
##
## REML criterion at convergence: -35.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.81571 -0.56402 -0.07279 0.57925 1.83611
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.02921 0.1709
## Residual 0.01106 0.1052
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.37623 0.06923 8.00000 5.434 0.00062 ***
## PC2_bark -0.07804 0.03571 8.00000 -2.185 0.06036 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2_bark 0.577## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 0.052816 0.052816 1 8 4.7756 0.06036 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.09486662 0.26188408
## sigma 0.08327577 0.13863234
## (Intercept) 0.24221620 0.51024783
## PC2_bark -0.14717167 -0.00891506## Analysis of Variance Table
##
## Response: k
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2_bark 1 0.61084 0.61084 17.13 0.0001866 ***
## Residuals 38 1.35505 0.03566
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = k ~ PC2_bark, data = s_crs_sm_tr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33234 -0.13523 -0.01884 0.12441 0.41205
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.37623 0.03656 10.292 1.53e-12 ***
## PC2_bark -0.07804 0.01886 -4.139 0.000187 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1888 on 38 degrees of freedom
## Multiple R-squared: 0.3107, Adjusted R-squared: 0.2926
## F-statistic: 17.13 on 1 and 38 DF, p-value: 0.0001866
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_ln$k + 2) ~ PC2_bark + (1 | species)
## Data: s_fine_sm_ln
##
## REML criterion at convergence: -104.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.3037 -0.6361 -0.1678 0.4249 2.5831
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.006002 0.07747
## Residual 0.002395 0.04894
## Number of obs: 44, groups: species, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.893660 0.031727 8.999999 28.17 4.36e-10 ***
## PC2_bark 0.002177 0.019829 8.999999 0.11 0.915
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2_bark -0.636## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 2.8868e-05 2.8868e-05 1 9 0.0121 0.915## 2.5 % 97.5 %
## sd_(Intercept)|species 0.04434103 0.11684607
## sigma 0.03914139 0.06361291
## (Intercept) 0.83212500 0.95519582
## PC2_bark -0.03628215 0.04063659## Analysis of Variance Table
##
## Response: log(s_fine_sm_ln$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2_bark 1 0.00032 0.0003183 0.0422 0.8382
## Residuals 42 0.31664 0.0075390##
## Call:
## lm(formula = log(s_fine_sm_ln$k + 2) ~ PC2_bark, data = s_fine_sm_ln)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.144141 -0.065180 -0.008046 0.041685 0.228663
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.893660 0.016954 52.711 <2e-16 ***
## PC2_bark 0.002177 0.010596 0.205 0.838
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.08683 on 42 degrees of freedom
## Multiple R-squared: 0.001004, Adjusted R-squared: -0.02278
## F-statistic: 0.04222 on 1 and 42 DF, p-value: 0.8382
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: log(s_fine_sm_tr$k + 2) ~ PC2_bark + (1 | species)
## Data: s_fine_sm_tr
##
## REML criterion at convergence: -105.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.82146 -0.60354 -0.05828 0.56890 1.78359
##
## Random effects:
## Groups Name Variance Std.Dev.
## species (Intercept) 0.004794 0.06924
## Residual 0.001756 0.04190
## Number of obs: 40, groups: species, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.86280 0.02801 8.00000 30.807 1.34e-09 ***
## PC2_bark -0.03111 0.01445 8.00000 -2.154 0.0634 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## PC2_bark 0.577## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## PC2_bark 0.0081441 0.0081441 1 8 4.6387 0.0634 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## 2.5 % 97.5 %
## sd_(Intercept)|species 0.03856875 0.105999534
## sigma 0.03317949 0.055235159
## (Intercept) 0.80858974 0.917012810
## PC2_bark -0.05907760 -0.003150597## Analysis of Variance Table
##
## Response: log(s_fine_sm_tr$k + 2)
## Df Sum Sq Mean Sq F value Pr(>F)
## PC2_bark 1 0.097088 0.097088 16.762 0.0002134 ***
## Residuals 38 0.220109 0.005792
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Call:
## lm(formula = log(s_fine_sm_tr$k + 2) ~ PC2_bark, data = s_fine_sm_tr)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.147448 -0.051113 -0.005561 0.050591 0.157434
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.86280 0.01473 58.561 < 2e-16 ***
## PC2_bark -0.03111 0.00760 -4.094 0.000213 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.07611 on 38 degrees of freedom
## Multiple R-squared: 0.3061, Adjusted R-squared: 0.2878
## F-statistic: 16.76 on 1 and 38 DF, p-value: 0.0002134###
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 0.1134,"%", " P = 0.02"))
fine_big_plot2<- ggplot()+ geom_point(data=s_fine_big, aes(y=k, x=PC2,color=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_fine_big, aes(y=k, x=PC2, colour=growth_form,linetype=growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_y_continuous(limits = c(0,1.5))+
labs(colour ="Growth forms", title = "(b) Inverterbrates blocked", y= " Decay rate (k) 4.0 cm WD)",
x= "Wood traits PC2")+
#annotate("text", x = 0, y = 1.2, size=3, label = lb1,parse=TRUE, colour= "lightcoral") +
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
fine_big_plot2
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_small<-s_fine[s_fine$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.16,"%", " P < 0.001"))
lb1b <- expression(paste("", R^2 , "= ", 9.96,"%", " P = 0.014"))
fine_small_plot2<- ggplot()+ geom_point(data=s_fine_small, aes(y=k, x=PC2, colour=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_fine_small, aes(y=k, x=PC2, colour=growth_form, linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+scale_y_continuous(limits = c(0,1.5))+
labs(colour ="Growth forms", title = "(a) Inverterbrates blocked", y= " Decay rate (k) 2.5 cm WD",
x= "Wood traits PC2")+ guides(shape=FALSE)+
annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = c(0.3,0.8),
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
fine_small_plot2
####
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_crs[s_crs$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 10.94,"%", " P = 0.03"))
crs_big_plot2<- ggplot()+ geom_point(data=s_crs_big, aes(y=k, x=PC2, colour=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_crs_big, aes(y=k, x=PC2, colour=growth_form,linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_y_continuous(limits = c(0,5))+
#annotate("text", x = 0, y = 1.2, size=3, label = lb1,parse=TRUE, colour= "lightcoral") +
labs(colour ="Growth forms", title = "(d) Inverterbrates access", y= "Decay rate (k) 4.0 cm WD)",
x= "Wood traits PC2")+ guides(shape=FALSE)+
annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
crs_big_plot2
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_small<-s_crs[s_crs$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 35.16,"%", " P < 0.001"))
lb1b <- expression(paste("", R^2 , "= ", 9.96,"%", " P = 0.014"))
crs_small_plot2<- ggplot()+ geom_point(data=s_crs_small, aes(y=k, x=PC2, colour =growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_crs_small, aes(y=k, x=PC2, colour=growth_form, linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 3))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_y_continuous(limits = c(0,5))+
labs(colour ="Growth forms", title = "(c) Inverterbrates access", y= "Decay rate (k) 2.5 cm WD)",
x= "Wood traits PC2")+ guides(shape=FALSE)+
annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
plt_pca2<- fine_small_plot2+fine_big_plot2+crs_small_plot2+crs_big_plot2
#ggsave(filename="plt_pca_k2.png", plot=plt_pca2, device="png",
# path=path, height=6, width=6, units="in", dpi=500)
###########################################################
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_big<-s_fine[s_fine$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 23.49,"%", " P = 0.001"))
fine_big_plot_brk2<- ggplot()+ geom_point(data=s_fine_big, aes(y=k, x=PC2_bark*-1,color=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_fine_big, aes(y=k, x=PC2_bark*-1, colour=growth_form,linetype=growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 1))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "#00E5EE") +
scale_y_continuous(limits = c(0,1.5))+
labs(colour ="Growth forms", title = "(f) Inverterbrates blocked", y= "Decay rate (k) 4.0 cm WD)",
x= "Bark traits PC2")+ guides(shape=FALSE)+
annotate("text", x = 3, y = 10, size=3, label = as.character(lb1),parse=TRUE, colour= "gray60") +
annotate("text", x = 3, y = 20, size=3, label = as.character(lb1b),parse=TRUE,colour="black") +
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
fine_big_plot_brk2
s_fine<- s[s$mesh_size == "Invertebrates blocked",]
s_fine_small<-s_fine[s_fine$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 30.61,"%", " P > 0.001"))
fine_small_plot_brk2<- ggplot()+ geom_point(data=s_fine_small, aes(y=k, x=PC2_bark*-1, colour=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_fine_small, aes(y=k, x=PC2_bark*-1, colour=growth_form, linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 1))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+scale_y_continuous(limits = c(0,1.5))+
annotate("text", x = 0, y = 1.2, size=3, label = as.character(lb1),parse=TRUE, colour= "#00E5EE") +
labs(colour ="Growth forms", title = "(e) Inverterbrates blocked", y= "Decay rate (k) 2.5 cm WD)",
x= "Bark traits PC2")+ guides(shape=FALSE)+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
fine_small_plot_brk2
####
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_big<-s_crs[s_crs$diameter_class == "5.0 cm",]
lb1 <- expression(paste("", R^2 , "= ", 23.85,"%", " P = 0.001"))
crs_big_plot_brk2<- ggplot()+ geom_point(data=s_crs_big, aes(y=k, x=PC2_bark*-1, colour=growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_crs_big, aes(y=k, x=PC2_bark*-1, colour=growth_form,linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 1))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
annotate("text", x = 0, y = 4.3, size=3, label = as.character(lb1),parse=TRUE, colour= "#00E5EE") +
scale_y_continuous(limits = c(0,5))+
labs(colour ="Growth forms", title = "(h) Inverterbrates access", y= " Decay rate (k) 4.0 cm WD)",
x= "Bark traits PC2")+ guides(shape=FALSE)+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
crs_big_plot_brk2
s_crs<- s[s$mesh_size == "Invertebrates access",]
s_crs_small<-s_crs[s_crs$diameter_class == "2.5 cm",]
lb1 <- expression(paste("", R^2 , "= ", 31.07,"%", " P < 0.001"))
crs_small_plot_brk2<- ggplot()+ geom_point(data=s_crs_small, aes(y=k, x=PC2_bark*-1, colour =growth_form), size=3,alpha = 0.6)+
geom_smooth(method = "lm",size=1, data=s_crs_small, aes(y=k, x=PC2_bark*-1, colour=growth_form, linetype = growth_form),se=FALSE)+
scale_fill_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
scale_linetype_manual (name = "Growth form",values = c("Lianas" = 3, "Trees" = 1))+
scale_colour_manual(name = "Growth form",values = c("Lianas"= "lightcoral", "Trees" = "#00E5EE"))+
annotate("text", x = -0.5, y = 4.3, size=3, label = as.character(lb1),parse=TRUE, colour= "#00E5EE") +
scale_y_continuous(limits = c(0,5))+
labs(colour ="Growth forms", title = " (g) Inverterbrates access", y= "Decay rate (k) 2.5 cm WD)",
x= "Bark traits PC2")+ guides(shape=FALSE)+
theme(axis.line = element_line(colour = "black", size = 0.5),
axis.ticks.x=element_line(colour = "black"),
axis.ticks.y=element_line(colour = "black"),
axis.text = element_text(colour = "black", size = 10),
axis.title = element_text(colour = "black", size = 12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(fill=NA,color="black", size=0.5, linetype="solid"),
panel.background = element_blank(),
strip.text = element_text(colour = "black", size = 12),
legend.direction = "vertical", legend.box = "horizontal",
legend.position = "none",
legend.text=element_text(size=10,face="bold.italic"),
legend.background = element_blank(),
legend.key.width=unit(0.2,"cm"),
legend.key.height=unit(0.2,"cm"))+
guides(fill="none")
crs_small_plot_brk2
plt_pca_brk2<- fine_small_plot_brk2+fine_big_plot_brk2+crs_small_plot_brk2+crs_big_plot_brk2
#ggsave(filename="plt_pca_k.png", plot=plt_pca, device="png",
# path=path, height=6, width=6, units="in", dpi=500)
plt_pca_wd_brk2<- fine_small_plot2+fine_big_plot2+crs_small_plot2+crs_big_plot2+
fine_small_plot_brk2+fine_big_plot_brk2+crs_small_plot_brk2+crs_big_plot_brk2+plot_layout(nrow = 2)
## Plot figure S8 decay rates against wood and bark traits PC2
ggsave(filename="Figure S8.png", plot=plt_pca_wd_brk2, device="png",
path=path, height=6, width=12, units="in", dpi=500)13 Conclusions
We compared wood decomposition rates of 12 lianas species and 12 tree species across different diameter classes in the presence and absence of invertebrates. After 24 months of decomposition, we found that liana wood decomposed faster than tree wood, and that the presence of invertebrates substantially increased wood decay. We also found that liana bark was more resistant to decay and persisted even after the xylem tissues was completely decomposed, which slowed liana decomposition rate. This study highlights the possible negative effects of liana proliferation on forest biogeochemical cycling and its implications for the potential of tropical forests to sequester and store carbon.